Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
Authors: Weimin Wu
Alexander C Furnas
Eddie Yang
Gefei Liu
Akhil Pandey Akella
Xuefeng Song
Dashun Wang
Han Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Benchmark
Dataset
Science
Policy
LLM
Primary area: datasets and benchmarks
TL;DR: The first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from a scientific paper.

Abstract: We propose Sci2Pol-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from a scientific paper.
We build Sci2Pol-Bench on a five-stage taxonomy to mirror the human writing process: 
(i) Autocompletion, (ii) Understanding, (iii) Summarization, (iv) Generation, and (v) Verification. 
It features 18 tasks in multiple-choice and open-ended formats.
Specifically, for the Generation stage, we show that BERTScore and ROUGE scores fail to capture the quality of brief writing, and introduce a new LLM-based evaluation metric aligned with expert judgement.
Using this benchmark, we evaluate 13 leading open-source and commercial LLMs to uncover key limitations.
To improve LLM performance on brief writing, we curate the Sci2Pol-Corpus for fine-tuning.
We start by linking each cited scientific paper to its corresponding policy document, drawn from 5.6 million policy records.
This produces 140,000 candidate pairs.
We then employ an LLM-as-a-judge to filter high-quality examples, followed by in-context polishing using three expert-written samples as references.
This process yields a final set of 639 new pairs.
Finally, we fine-tune three models on Sci2Pol-Corpus: LLaMA-3.1-8B, Gemma-12B, and Gemma-27B.
Fine-tuning leads to consistent performance improvements across Sci2Pol-Bench.
Notably, after fine-tuning, Gemma-27B surpasses the much larger GPT-4o and DeepSeek-V3 (671B).
These demonstrate the effectiveness of our corpus in bridging the gap between science and policy.

Published as a conference paper at ICLR 2026
SCI2POL: EVALUATING AND FINE-TUNING LLMs ON -CIENTIFICTTO-POLICC BRIEF GENERATION
Weimin Wut.1 Alexander C. Furnas Eddie Yang! Gefei Liu
Akhil Pandey Akella? Xuefeng Song" Dashunn Wangi*2 Han Liu 18.3
f Center for Foundation Models and Generative AI, Northwestern University, IL 60208, USA Department of Computer Science, Northwestern University, IL 60208, USA
$ Center for Science of Science and Innovation, Northwestern University, IL 60208, USA Kellogg School of Management, Northwestern University, IL 60208, USA
b Department of Computer Science, Brown University, RI 02912, USA
& Department of Statistics and Data Science, Northwestern University, IL 60208, USA
1 wwmêu www.hneeteeeer..cduu
2 dashun wang www.:moootthwtrreemeeeeee
han h
ABSTRACT
We propose Sci2Pol-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from scientific paper. We build Sci2Pol-Bench
sive-stage taxonomy to mirror the human writing process: (i) Autocompletion, (ii) Under- standing, (iii) Summarization, (iv) Generation, and (v) Verification. It features 18 tasks multiple-choice and open-ended formats. Specifically, for the Generation stage, show that BERTScore and ROUGE scores fail to capture the quality of brief writing, and introduce a new LLM-based evaluation metric aligned with expert judgement. Using this benchmark, we evaluate 13 leading open-source and commercial LLMs to uncover key limitations. To improve LLM performance on brief writing, we curate the Sci2Pol- Corppu for fine-tuning. We start by linking each cited scientific paper to its corresponding policy document, drawn from 5.6 million policy records. This produces 140,000 candidate pairs. We then employ an LLMeaa-a--jdgee to filter high-quality examples, followed by in-context polishing using three expert-written samples as references. This process yields a final set 639 new pairs. Finally, we fine-tune three models on Sci2Pol-Corpus LLaMA-3.1- 8B, Gemma- 12B, and Gemma-22 22B. Fine-tuning leads to consistent performance improvements across Sci2Pol-Bend Bench. Notably, after fine-tuning, Gemma-27B surpasses the much larger GPT-4o and DeepSeek- V3 (671B). These demonstrate the effectiveness of our corpus in bridging the gap between science and policy.
Project Page: hntpps: / /ttgtttttuu////// com/ Ne:miin/12200//5522220 Keywords: Benchmark, Dataset, Science, Policy, LLM
INTRODUCTION
We propose Se12Pll-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evalu- ating and fine-tuning LLMs on scientifie-to-policy brief generation. A policy brief is a concise article that distills the content of a technical scientific paper for policymaker audience (Appendix C). Turn- ing scientific evidence into policy remains critical and difficult for both policymakers and scientists. Today's major challenges (e.g., climate change, public health, and rapid technological shifts) require timely input from science (Wang and Barabási 202T). Yet policymakers often struggle to convert dense, technical research into clear and usable guidance. This issue is also relevant to the scientific community, as it underscores the essential role of science in shaping societal outcomes. However, most scientists lack policy expertise. This gap limits how science informs real-world decisions (ttra et al. 2012). With the rise of powerful LLMs, we ask two key questions: (i) To what extent can LLMs assist in scienlyficto--oolccy brief generation? (ii) How their performance be further
Co-corresponding authors.

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Published as a conference paper at ICLR 2026
Nature Sustainabiiity Nature Cities
Checking factual consistency
Verfi sutin
25
Drafting policy
Generation
Journal of Health
Condensing technical passages
Summarization
Behavior
(JMSB)
Identifying elements
Understanding
Completing missing content
Autocomplation
Energy
(a) Sci2Pol- Sci2Pol-Taxomomy
(b) Dataset Source
Figure 1: Overview of Sci2Pol-Taxonon and Dataset Source. (a) Sei2Pol-Taxonone defines five-
stage decomposition of the brief writing process. (b) Complete set of published 85 pairs.
improved? To address these, we introduce Sci2Pol-Bende and Sci2Pol-Corpus. Sci2Pol-Bench offers the first comprehensive benchmark for fine-grained evaluation of scientific-to-polic generation. Sci2Pol-Corpus provides the first targeted training dataset to enhance LLM performance this task.
Although LLMs demonstrate strong general capabilities, prior work shows they hallucinate scientific content, misverify claims, and produce unstable or biased policy reasoning (Coz al. 2025 Bai
ct al. 2025 Wadden et al., 2024). To further validate these concerns, we start with expert-reviewar
examples and highlight four key limitations in policy brief generation (Appendix FF: (i) Missing
content: LLMs often fail to capture a study's s essential details, including its quantitative findings, methods, and broader context. They omit key facts add irrelevant information (see Appendix [TT).. (ii) Hallucinated claims: L.MS invent numbers or causal statements that do not appear in the source paper (see Appendix E22. (iii) Inappropriate tone: Even when accurate, the language is often too technical or overly verbose for policy audiences (see Appendix F3) (iv) Low actionability:
Recommendations tend to be vague and weakly supported by evidence (see Appendix F.4)..
Rigorous evaluation in this domain requires a clear decomposition of the writing process and dataset that challenges models with authentic, domain- matched targets. To this end, we define the Scientific-to-Polie Taxonomy Sei2Pol-Taxonons
Figure Ta), five-stage framework for the brief writing workflow: (i) Autocompletion: Completing missing content in scientific or policy texts; (ii) Understanding: Identifying and interpreting key claims, caveats, and methods; (iii) Summarization: Condensing technical passages into language accessible to policy audiences; (iv) Generation: Drafting coherent, persuasive prose that integrates evidence with policy context; (v) Verification: Checking factual consistency of claims or drafts against the source literature.
Grounded in this taxonomy, build Sci2Pol-Benchh suite of 18 diverse tasks for evaluating model performance in generating policy briefs from scientific research. We construct domain-specnneccco sspciiii targets from 85 expert-written paper-brief pairs, representing the complete set of published pairs. These pairs are drawn from high-impact venues, including Nature Energy, Nature Climate Change, Nature Cities, Nature Sustainability, and the Journal of Health and Social Behavior. Figure Ib presentss summary statistics of these pairs. Each task takes the form of either a multiple-choice probe or an open-ended format. They enable fine-grained evaluation along both correctness and writing quality. For the Generation stage, we show that BERTScore and ROUGE scores fail to capture the quality of the brief writing, and we design dedicated evaluation metric using an LLM-as-au ju-j-e approach. We then conduct the first large-scale evaluation of scientific-to-polle covering both open-source and commercial models. Our results show that even advanced L.LMs such as Grok and DeepSeek-RI leave substantial room for improvement. These findings highlight concrete research opportunities in controllable generation and domain adaptation for policy applications.
brief generation across 13 L.LMs,
To further improve LLM performance policy brief generation, we introduce Sci2Pol-Corpin the first training dataset designed for this task. The construction process consists of two resource- intensive steps followed by novel polishing step. (i) Retrieving candidate science-policy pairs: We begin with large-scale collection of 5.6 million policies indexed by Overton and scraped as PDFs from the public websites that published them (e.g., Government Printing Office, Congress.gov
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). From these, we construct 140,000 citation-basee candidate pairs by linking policy documents to their cited scientific papers. To ensure tighter relevance, filter for policy files that cite more than three papers and treat each citation as potential paper-brief pair. (ii) Filtering high-quality pairs with an L.LM-as-a-judge approach: We employ GPT-o3 evaluate whether each policy document is centered on the cited scientific paper. This two-stage filtering process yields 639 high-quality pairs. (iii) Refining brief's through in-context revision: To further align the collected briefs with the style and structure of expert-written examples, we select three high-quality samples from the 85 published pairs. We then use GPT-o3 in an in-context learning setup to revise the 639 identified policy briefs. This step ensures their tone, structure, and clarity match the expert-written briefs. Finally, we use Sci2Pol- Corpus to fine-tune three open-source LLMs: LLaMA-3. --8--Instrct, Gemma-12B-Insee Inn-Intt and Bemma-22B-Instreeee Fine-tuning Sci2Pol-Corpus leads to consistent performance improvements across 1i22oll-ench. Notably, after supervised fine-tuning, Gemma-277 2aaaa--7BB--nuuu II--tnstucct surpasses the much larger GPT-4o and DeepSeck-V3 (67IB). These demonstrate the effectiveness of our corpus in scientific-toppolic brief generation.
The Brookings Institution, The World Bank) (Furnas al. 2025 Szomszor and Adie 2022
In summary, have the following three contributions:
We propose Sci2Pol-Bench, the first comprehensive benchmark for evaluating LLMs on policy brief generation from scientific papers. We build the benchmark framework, Sci2Pol- Taxoooo,, and include the complete set of 85 published expert-written paper-brief pairs. It features 18 tasks and provides the first large-scale evaluation across 13 LLMs.
five-stage workflow
We curate Sci2Pol-Corpus, the first training dataset for fine-tuning L.MSs on policy brief genera- tion. It consists of two resource-intensive steps followed by novel polishing step: (i) retrieving 140,000 candidate science- policy pairs by linking cited scientific papers across 5.6 million policy documents; (ii) selecting 639 high-quality pairs using an LLM-as-a-judge filtering method; and (iii) enhancing these pairs via in-context revision to improve clarity and alignment.
We use Sei2Pol-Corpus to fine-tune three open-source LLMs: LLaMA-3.1-800 1------nnnstett, Gemma- 12B-Instruct, and Gemmma-27B-Instruue Fine-tuning leads to consistent performance improve- ments across Sei2Pol-Bench. Notably, after supervised fine-tuning, Gemma-27B-Inmg IIstrrctt sur- passes the much larger GPT-4o and DeepSeek- V3 (671B). These demonstrate the effectiveness of Sci2Pol-Corpus enhancing LLM capabilities for scientific-to-polig brief generation.
Organization. Section 2 introduces Sci2Pol-Bench. Section 3 details the Sci2Pol-Corpus. Section shows the evaluation results across 13 LLMs, and supervised fine-tuning results.
2
SCI2POL-BENCH
We detail our Sci2Pol-Bench here. Section 2.1 outlines the core design principles. Section 2.2 describes the data collection and processing pipeline. Section 2.3 presents the task definitions and summarizes the dataset. Section 2.4 details the evaluation metrics, with particular focus on an LLM-based evaluation metric for Generation-related tasks to better align with expert judgement.
2.1 DESIGN PRINCIPLE
The overall goal of Cei2Pol-Bench is to provide researchers and practitioners with a transparent, fine-grained assessment of how well LLMs can translate dense scientific research into actionable policy briefs. Inspired by the progressive, ability-oriented evaluation framework of et al. (2024a), we introduce five-stage Sci2Pol-Taxonome to mirror the workflow of brief writing.
As illustrated in Figure Ta the taxonomy defines five levels of ability: (i) Autocompletion tasks require LLMs to predict continuations or recombine sentences. This tests their grasp of local cohesion and textual fluency. (ii) Understanding tasks involve classifying sentence intent and answering multiple-choice questions. This evaluates the model's factual comprehension and ability to process complex research narratives. (iii) Summarization tasks focus distilling scientific text into concise summaries. These tasks assess the ability to extract salient points and deliver them. (iv) Generation tasks ask LLMs to compose new policy brief content from scratch. This requires synthesizing scientific evidence, contextual understanding, and persuasive framing into coherent, structured writing. (v) Verification tasks challenge models to fact-check scientific or policy-related claims against the source literature. This critical for mitigating hallucinations. Following the five stages, Sci2Pol-Besd Bench provides robust framework evaluate the L.MSs in bridging science and policy.
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Table 1: Summary Sci2Pol-Bench. Sci2Pol-Bench comprises 18 tasks. Each task specifies an ID, task description, data source, sample size, and evaluation metric.
Taxonomy
ID
Task Description
Source
Size
Metric
Scientific Text Autocompletion
Our Dataset 255
Micro Fl
Political Text Autocompletion
Our Dataset
255
Micro FI
Autocompletion
Scientific Sentence Reordering
Our Dataset
255
Micro FI
Political Sentence Reordering
Our Dataset 255
Micro Fl
Sentence Classification
Our Dataset
1200
Micro F1
Understanding
Scientific Knowledge Unddesttandig
MML.UPro
1000
Micro F1
7 Policy Problem Summarization
Our Dataset
200
Reference- frreeneee-reeeeeeeee Score
Research Findings Summarization
Our Dataset
200
Reference-free Score
Summarization
Study Methods Summarization
Our Dataset
200
Reference-free Score
10
Policy Implications Summarization
Our Dataset
200
Reference- free Score
11
Policy Problem Generation
Our Dataset
Reference-based Score
12
Research Findings Generation
Our Dataset
Reference-hased Score
Generation
13
Study Methods Generation
Our Dataset
Reference-based Score
14
Policy Implications Generation
Our Dataset 85
Reference-base Score
15
Policy Brief Generation
Our Dataset 85
Reference-based Score
16
Scientific Claims Verification
Our Dataset
850
Micro Fl
Verification
17
Scientific Claims Verification 2 Policy Implications Verification
SciRIFF
1000
Micro Fl
18
Our Dataset
700
Micro F1
2.2 DATA COLLECTION AND PROCESSING
Data Sources. Cei2Pol-Bench draws from two sources: (i) Existing benchmarks: We incorporate tasks from established datasets focused on scientific understanding. including SciRIFF (Wadden et al., 2024) and MMLU-Pro (Wang et al. 2024). (ii) Newly collected pairs: We curate the expert-authored paper-brief pairs published across top journals, as summarized in Figure Ibb To ensure data quality, apply strict inclusion criterion: the policy brief must be written by the same authors as the original scientific paper. This guarantees that the policy content reflects authentic expert interpretation. The final collection consists of 85 high-quality pairs (see Appendix [J. for the full list).
Data Processing and Annotation. We download each article's ' metadata and full text to maintain quality control. Most policy briefs share titles with their corresponding scientific articles, little disambiguation. We apply OCR to extract text from PDFs and use a light preprocessing script remove tabs, extra spaces, and stray characters. We store all data in structured JSON format.
face
2.3
TASK DEFINITION AND DATASET SUMMARY
Two tasks come from existing datasets, and experts construct the remaining sixteen. We describe each task in detail below.
Guided by the Sci2Pol-Taxonomy we construct 18 evaluation tasks (Table D.
2
AUTOCOMPLETION (TASKS 1-4)
These tasks probe local coherence: given short scientific or policy passage, the model selects the next sentence (multiple choice) or restores shuffled sequence.
Task 1: Scientific Text Autocompletion (Multiple Choice). This task tests local discourse coherence in scientific writing. The dataset contains 255 items drawn from 85 articles, with 3 items selected per paper. We construct each example in two steps: (i) Extract a sequence of three consecutive sentences from the article. Use the first two sentences as the prompt and the third as the gold (correct) continuation. (ii) Construct five candidate continuations for each prompt: one gold sentence (the true continuation) and four distractor sentences sampled from the nearby context in the same paper. We ask the model to choose the correct answer from five options. See Table 45 for an example.
Task 2: Political Text Autocompletion (Multiple Choice). This task tests coherence in policy writing. The dataset contains 255 items from 85 professional policy briefs, with 3 items selected per brief. We construct each example in two steps: (i) Extract sequence of three consecutive sentences from the brief. Use the first two sentences as the prompt and the third as the gold (correct) continuation. (ii) Construct five candidate continuations for each prompt: gold sentence (the true
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continuation) and four distractor sentences sampled from the nearby context in the same brief. We ask the model to choose the correct answer from five options. See Table for an example.
Task 3: Scientific Sentence Reordering (Recombination). This task probes discourse-level coherence in scientific writing. We reuse the 255 three-sentence triplets from Task 1, shuffle each triplet, and ask the model to restore the original order. See Table for an example.
Task 4: Political Sentence Reordering (Recombination). This task probes discourse coherence in policy briefs. We reuse the 255 three-sentence triplets from Task 2, shuffle each triplet, and ask the model to restore the original order. See Table 48 for an example.
2.3.2 UNDERSTANDING (TASKS 5-6)
Tasks 5-6 evaluate sentence-level comprehension and scientific knowledge. Task classifies sentences from scientific papers into five policy brief-relevant categories. Task 6 evaluates scientific knowledge.
Task 5: Sentence Classification (Multiple Choice). This task assesses the classification of policy brief-relevant sentences by rhetorical role content. The dataset includes 1,200 manually verified samples from paper-brief pairs. We curate in three steps: (i) For each pair, use a templated prompt (see Appendix L.7 generate 15 labeled examples with GPT-o3; (ii) Political experts manually review labels and language quality; (iii) Select a random subset of 1 1,200 samples. Each instance consists of one sentence and label from a fixed (e.g., Policy Problem, Scientific Research Findings, Scientific Research Study Methods, Policy Implications, None). See Table 49 for an example.
Task 6: Scientific Knowledge Understanding (Multiple Choice). This task evaluates broad scientific knowledge with multiple-choice questions from MMLU-Pro (Wang et al. (2024). The full subset contains 3 questions across health, chemistry, economics, and biology. We treat these as one task and sample 1,000 questions randomly for evaluation. This baseline enables comparison between ceneral-purposse and science-specializee models. See Table for an example.
2.3.3 SUMMARIZATION (TASKS 7-10)
Tasks 7-10 evaluate policy-oriented summarization. We show the details follows.
Task 7: Policy Problem Summarization (Writing). We evaluate the ability to identify and summarize the policy problem motivating a study. The dataset includes 200 examples. We curate the dataset following three steps: (i) For each pair, select up to three paragraphs relevant to the brief's policy problem; (ii) If fewer exist, include all reliably matched paragraphs: (iii) From the final 233 paragraph sets, sample 200 randomly for evaluation. See Table 51 for an example.
Task 8: Research Findings Summarization (Writing). We evaluate the summarization of core scientific findings. The dataset includes 200 examples. We curate the dataset following two steps: (i) For each pair, select three paragraphs aligned with the brief's findings; (ii) From 255 sets, sample 200 randomly for evaluation. See Table 52 for an example.
Task 9: Study Methods Summarization (Writing). We evaluate the summarization of methodology in policy brief-relevant terms. The dataset includes 200 examples. We curate it following two steps: (i) For each pair, select three paragraphs that describe the methodology reflected in the brief; (ii) From 255 sets, sample 200 randomly for evaluation. See Table 53 for an example.
Task 10: Policy Implications Summarization (Writing). We evaluate the articulation of policy implications grounded in the source paper. The dataset includes 200 examples. We curate the dataset following three steps: (i) For each pair, select up to three paragraphs that support the brief's implications; (ii) If fewer exist, include all reliably matched paragraphs; (iii) From 222 sets, sample 200 randomly for evaluation. See Table 54 for an example.
2.3.4 GENERATION (TASKS 1 11-15)
These tasks evaluate brief generation: policy problem, findings, methods, implications, and full brief. We provide detailed justification for separating section-by-section generation (Tasks 11-14) from full-briet generation (Task 15) in Appendix H.7] In short, Tasks 11-14 complement Task 15 by disentangling factual precision from holistic coherence. Section-level generation emphasizes accurate grounding. while full-brief generation assesses overall readability. Evaluating both provides a more comprehensive view of I.LM performance and uncovers trade-off' that Task 15 alone can not capture.
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Task 11: Policy Problem Generation (Writing). We generate the Policy Problem section from the full scientific paper (85 examples). For each pair, we extract the brief"ss s Policy Problem as a reference and provide the full paper as input. For 20 Journal of Health and Social Behavior pairs, they lack clear Policy Problem section. We prompt GPT-o3 (see Appendix [_.8) to construct the Policy Problem section with inputs: the full paper, the brief's Rexearch Problem Data section, and three in-context expert-written examples from the remaining 65 Nature journals. See Table (5 for an example.
Task 12: Research Findings Generation (Writing). We generate the Research Findings section from the full paper (85 examples). For each pair, we extract the brief"s Research Findings as reference and provide the full paper as input. See Table an example.
Task 13: Study Methods Generation (Writing). We generate the Research Study section (85 examples). For each pair, we extract the brief's Research Study as reference and provide the full paper as input. For 20 Journal of Health and Social Behavior pairs, the briefs lack a clear Research Study section. We prompt GPT-o3 (see Appendix [..9 to construct the Research Study section using the full paper, the brief's s Research Problem & Data section, and three in-context expert werttee examples from the remaining 65 Nature journals. See Table for an example.
Task 14:
Policy Implications Generation (Writing). We generate the Policy Implications section (85 examples). For each pair, we extract the ground-truth policy implications as a reference and provide the full scientific paper as input. See Table 55 for example.
Task 15: Policy Brief Generation (Writing). We generate an entire policy brief (85 examples). For each pair, we build the reference by concatenating Title, Policy Problem, Research Findings, Research Study, and Policy Implications. Input is the full paper. See Table for an example.
2.3.5 VERIFICATION (TASKS 16- 18)
Tasks 16-18 assess consistency between claims (findings or policy implications) and the source paper.
Task 16: Scientific Claims Verification (Multiple Choice). We evaluate whether scientific claim supported by the paper (850 samples). We construct in two steps: (i) For each paper. prompt GPT-o3 (see Appendix 10) to generate 10 labeled samples (total 850); (ii) Our political experts manually review all samples and correct three issues. See Table for an example.
Task 17: Scientific Claims Verification 2 (Multiple Choice). We evaluate claim-evidence entailment using SciRIFF subsets (Wadden et al., 2024): covidfact entailment, healthver entailment, and scifact entailment (1,220 samples). We sample 1,000 randomly for evaluation. Each instance presents claim (e.g., support) and associated evidence. See Table 61 for an example.
Task 18: Policy Implications Verification (Multiple Choice). We evaluate whether policy implication follows from the paper (700 samples). We construct in four steps: (i) Extract all implications from each brief and label them support; (ii) Prompt GPT-o3 to generate contradicted implications (see Appendix [T and label them contradict after manual review of our experts; (iii) Combine yield 706 samples; (iv) Sample 700 randomly for evaluation. Each instance includes paper, a policy implication, and a label (support or contradict), See Table 52 for an example.
2.4 EVALUATION METRICS
In this part, we report detailed evaluation metrics for each task, with a particular focus on Tasks 11-15.
Micro F1 (Tasks 1-6, 16-18). We compute per-item correctness and use Micro-Fl as the main score. We choose this metric for classification tasks with a firm correct answer. This group includes autocompletion (Tasks 1-4), understanding (Tasks 5-6), and verification (Tasks 16-18).
Reference-free Score (Tasks 7-10). We use Gemini-2.5-Pro as an LLM judge to score section summaries. The judge evaluates four dimensions of summary: clarity, accuracy, coverage, and overall quality. We select this metric because these tasks involve free-form writing without a single referenced correct answer. See Appendix [.. for the full prompt and calculation details.
For Tasks 11 11-15, we first demonstrate the limitations of BERTScore and ROUGE scores, and then introduce task-specific reference- based score for more accurate evaluation.
Limitations in BERTScore and ROUGE Scores for Tasks 11-15. As shown in Appendix H.I BERTScore remains high even when key sections are missing, overlapping words inflate similarity.

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ROUGE scores penalize paraphrasing and drop sharply with minor grammatical changes, despite preserved meaning. Neither metric captures reasoning, structure, or evidence linkage.
Reference-based Score (Tasks 11-15). We evaluate the generation tasks using content- aware LLM judging. guided by paper-grounded rubrics that rely on both the paper and policy brief sections.
Task 11 (Policy Problem). We score by content and structure because policy problem contains linked and causal sentences. We describe five parts as the full space of content in a policy problem. but any subset may appear, and the order may vary. (i) Background sets the scene. (ii) Existing problem states the current obstacle. (iii) Consequences describe risks if the problem stays unsolved. (iv) Atlention problem names the issue that calls for action. (v) Supporting detail adds facts, numbers, or sources that help this flow. For each part, we judge two things: its importance in the paper and its quality in the candidate. This checks what to say and how well it is said, balancing relevance and quality. See Appendix L.2 for the full prompt and calculation details.
Task 12 (Research Findings). We score by content only because findings are mostly independent. The judge rates five aspects. We check (i) completeness, (ii) importance, and (iii) accuracy of the candidate findings. (iv) Summarizing findings checks if the text highlights the key results rather than long list. (v) Specification to findings checks scope, context, and limits. This rubric rewards correct, essential, and well-focused content. See Appendix L..3 for the full prompt and calculation details.
Task 13 (Study Methods). We score by content only because method points are independent. The LLM judge rates three aspects. (i) Clarity and purpose checks if the text explains what method is used and why, in a clear output. (ii) Technicality appropriateness checks if the level of detail fits policy audience without jargon. (iii) Explanation of terms checks if models, data, and acronyms are explained in plain words. Note that clarity and purpose and technicality appropriateness carry more weight in evaluation, because explanation of termy only serves as an extra signal. This rubric rewards clear intent, appropriate detail, and good definitions. See Appendix L.4 for details.
Task 14 (Policy Implications). We score by content only because implications are written as separate points. The LLM judge rates four aspects. (i) Accuracy checks if the implications are supported by the paper without speculation or ballucination. (ii) Coverage checks if all major implications are included. (iii) Conciseness and distinctness checks if each implication is concise and non non-redundant. (iv) Alignment with paper intent checks if the implications match the paper's 's main message, such as a recommendation, warning, or call awareness. This rubric rewards grounded, complete, and actionable implications. See Appendix L.5 for the full prompt and calculation details.
Task 15 (Full Policy Brief). We score by content and style together. The LLM judge rates four aspects. (i) Contextual depth checks if the brief captures key findings, methods, and context without missing facts or adding fluff. (ii) Hallucination risk checks if every claim is traceable to the paper, with penalties for unsupported numbers or causal links. (iii) Readability tone checks if the text is concise, structured, active, and suitable for policymakers. (iv) Actionability checks the implications concrete, tied to evidence, and useful for policy. This rubric rewards briefs that are accurate, clear, credible, and practical. See Appendix [.6 for the full prompt and calculation details.
SCI2POL-CORPUS
In this section, we give the details of the Sci2Pol-Corpus. It comprises 639 high-qualit, paper-briet pairs curated from 5.6 million policy documents. It includes three steps: (i) Retrieving candidate science-policy pairs (Section 3.T); (ii) Filtering high-quality pairs with an LLM-as-a-judge approach (Section 3.2); (iii) Polishing briefs through in-context revision (Section 3.3
3.1 CANDIDATE PAIR RETRIEVAL
We begin with a large-scale political dataset collection. This collection is derived from documents indexed by Overton, the world's largest database of policy literature(Furnal et al. 2025 Szomszor and Adie 2022: Yin et al.) 2022). From there, we retrive pdf and html policy documents from the public websites of the original publishing organizations and the United States Government Printing Office, IGOs like, the OECD, the UN, and the WHO and numerous think tanks like the Brookings Institution and the Heritage Foundation. These documents cover a broad range of domains, topics and geographic regions. They provide a rich foundation for identifying scientific publications cited in real-world policy contexts. Leveraging Overton's 's citation metadata, we identify the scientific papers cited by each policy document, Each citation forms a candidate paper-brief pair, and links scientific
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(i) Candidate Pair Retrieval
(II) LLM-based Quality Filtering
(III) In-context Polishing
Coarse-grained filtering)
5.6M Policy
GPT-03
Sci2Pol- Bench
3 References
Documents
Filter on alignment:
scientific paper abstract
Overton Citation
v.s. policy document
Structure and style palicy
Gitations
Fine-grained filtering
document into policy brief.
Metadata
Filter on aligement: full scientific paper v.s. policy document
639 Paper-brief Pairs
140K Candidate
Science-policy Pairs
639 High-quality Pairs
Sci2Pol-Corpus
Figure 2: Overview of the SciZPol-Corpar Curation Process. It consists of 639 paper-briel pairs. Pair retrieval relies on the original policy documents and the scientific papers they cite as candidates.
publication to policy document that references it. To prioritize relevance, we apply a heuristic: The fewer scientific papers a policy document cites, the more likely is focus on each one. This assumption increases the likelihood that the policy document reflects or interprets the cited scientific content. Based on this insight, we retain only policy documents that cite no more than three scientific papers. This yields high-quality pool of 140,000 candidate pairs for further filtering.
3.2 LLM-BASED QUALITY FILTERING
In this stage, we employ GPT-o3 for automated quality filtering to assess whether the policy document centers on the scientific content. It extracts 639 high-quality pairs from 140,000 candidate pairs with two steps: (i) Coarse-grained filtering based on the alignment between the scientific paper abstract and the policy document, and (ii) Fine-grained filtering based on the alignment between the full scientific paper and the policy document. The rationale for this design
reduce the filtering cost.
Coarse-grained Filtering. Scientific papers are often long, typically over 10 pages, and sometimes more than 30. Assuming 500 words per page, this corresponds to about 5,000 to 15,000 words per paper. Assuming each word maps to one token in the GPT-o3 embedding layer, the total token count for 140,000 papers exceeds 700 million. At rate of $2 per million tokens, the cost of processing full texts becomes very high. To reduce cost while preserving essential information, we use only the paper abstracts this filtering stage. We extract these abstracts from SeiSciNet (L.in et al., 2023). paper is not found in SciSciNet, we discard the corresponding candidate pair. Given a scientific paper abstract and its associated policy document, we prompt GPT-o3 to assess whether the policy document centers on the scientific content described in the abstract. See Table 75 for the detailed prompt of GPT-o3. As a result, we obtain 1,407 potential high-quality pairs from this step.
After the GPT-o3-baser based filtering, observe that some policy documents are too long and exceed 10 pages. For standard policy briefs, we prefer shorter documents. typically fewer than 10 pages. Among the 1,407 pairs, 777 pairs contain policy documents under 10 pages, while 630 pairs involve longer documents. To make use of the 630 longer policy documents, we manually extract their executive summaries when available and treat these as the pseudo-policy briefs. For each such case, we constructed two pairs: (i) the executive summary paired with the corresponding scientific paper, and (ii) the executive summary paired with the remaining portion of the policy document. In the second case, the remaining text serves as a pseudo-scieen scientific paper, since it often delivers science-related technical details. If long policy document did not contain a summary, we discarded it. After this process, we retain 234 usable pairs from the long policy documents. Combined with the 777 pairs involving policy documents under pages, this yields a total of 1,011 curated pairs.
Fine-grained Filtering. We use GPT-o3 with the full scientific paper and the policy document for fine-grained filtering. The main goal is still to verify whether the policy document centers on the scientific content. However, some pairs originate from the same long policy document, where the executive summary serves as a policy brief and the remaining content acts as a proxy for the scientific paper. We need to avoid these pairs if the two texts are too similar. To handle this, add new criterion that measures similarity between the paper and the policy document. This step goes beyond the original metrics used in coarse-grained filtering. See Table 76 the detailed GPT-o3 prompt.
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Table 2: Performance of LLMs on Sci2Pol-Bench. We report average scores for 13 LLMs across the five categories of Sci2Pol- Sci2Pol-Taxonome Taxonomy. Tasks 1-6 and 16-18 use Micro F1. Tasks 7-10 use a reference free score, while Tasks 11-15 use a reference-basee score. Both of these are judged on Gemini-2.5-PP
Sci2Pol- PaaPoo-Txxooomy
Avg.
Runk
Model
Auto. (1-4)
Under. (5-6)
Sum. (7-10)
Gene. (11-15)
Ver. (16-18)
Grok-3-beta
50.77:22.89
80.12:41.22
83.26=0.05
86.70::0.98
85.45::0.88
77.01=:1.20
DeepSeck-RI
44.7643.11
86.6111004
80.83=0.04
84.75.1.226
83.841:.05
75.05=1.344
Qwen3-235B
47.22443.03
87.19-0.94
77.02-0.15
84,80-1
83.76-10.99
74.81+1..44
3
DeepSeek-VS. V3
39.54-43.06
19.35++1.28
78.97=0.05
86.23+11.26
85.48++0.85
13.3
GPT-4o
52.1743.00
77.17:1.32
74.23=0.06
76.39:11.28
85.45::0.82
12.12:1..32
Gemma-3-277
43.60-12.83
67.82++142
74.55=0.05
84.82:21116
84,29-10.98
11.40:
Claude-3.-Soomee
44.06++3.00
80.06+1.199
82.71-0.05
73.59+3.611
83.24++1.00
11.38+1.99
Mistral-Large
44.09:12.92
1..27::1.23
78.57=0.05
75.09:11442
81.87:11.111
1..2
LLaMA-3,3-70--TT
53,16:12.72
14.141:.33
71.22=0.06
69.89+1162
85.71=-0.87
69.58:1133
9
LaaMA---Maverick
38.74-42.90
83.81++1001
7247-0006
74.95++1.38
84.16-4-0.9
68.87-11.31
10
Qwen3-8B
35.154:2.88
80.84+1..21
74.08=0.17
77.79-+1.99
81.87:+1.01
68.51=1.39
Gemma-3-12B
42,96:12.79
1..61
71.79=0.05
77.34=1444
82.51=1106
68.47
12
LLaMA-3.1-8B-IT
27.12:42.53
4..74:11.54
64.42=0005
65.78:11.71
76.25++1.27
56.63+1143
13
Autocompletion
Autocompletion
90
90
80
80
70
70
60
60
Understanding
Verification
Understanding
50
Verification
40
30
Deepseak:Rt
30
20
Owen3-23b8
20
Deepreek-V3
Germma-3-276
Mistral-Large
LLaMA-3.3-70B-1
LLaMA.4 Maverick
Gemma:3- 3- 1-B
GPT-4o
Qwen3-88
C Caude-3.7 7-Sonnet
Caude-3.-88omee
LLaMM-331-8B-1 1-8B-T
LLaM4-3,
Summarization
Generation
Summarization
Generation
(a) Commercial LLMs
(b) Open-Source LLMs
Figure 3: Visualization of Performance of 13 LLMS Sci2Pol Bench. We show the average performance of commercial and open source L.MSs across the five categories of the Sci2Pol- Sci2Pol-Taxonomy Taxonomy.
3.3 IN-CONTEXT POLISHING
The last step polishes the policy document. Our policy documents come from official organizations and focus on policy, not briefs. Their format and style do not match a standard policy brief. We propose in-context polishing align them. We select three sxpert-writted paper-brief pairs from the 85 pairs in Sci2Pol- document to GPT-o3 and ask it revise the document into standard policy brief while preserving facts and citations. See Table 777 for the detailed prompt. This step does not inject scientific or political content from the three expert-written paper-brief pairs. only follows their writing style and format. To further validate that this step does not introduce information leakage from Sci2Pol-Bench we conduct additional experiments, with details provided in Appendix H.9
as references. We then provide the scientific paper and the policy
EXPERIMENTAL STUDIES
In this section, we present the performance L.LMs on Sei2Pol-Bench (Section 4.1 and demonstrate the performance gains achieved through supervised fine-tuning on Sci2Pol-Corpus (Section 4.2)
4.1 LLMS PERFORMANCE ON SCI2POL-BENCH
We evaluate 13 models, including both commercial and open-source models: (1) ChatGPT-4o, (2) Claude-3.7 3.7-Sonnet, (3) Gemma-3-22 3-2BB, (4) Gemma-3-12b, (5) Grok-3-beta, (6) DeepSeck-R1, (7) DeepSeek-V3. (8) Owen3-235B-A22 (9) Qwen3-8B, (10) Mistral-Large (11) 4

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Published as a conference paper at ICLR 2026
Table 3: Performance LLMs after Supervised Fine-tuning (SFT) on Sci2Pol-Corpus. We fine-tune three models: LLaMA-3.1 L.aMA-3.1885 1-8B-Instruct Gemma-3-122 and Gemma-3-27B on Sci2Pol-Corpus, and report their average performance across the Sii2Pol-Taxonon before and after fine-tuning.
Sci2Pol-Taxomaa
Avg.
Gain
Model
Auto. (1-4)
Under. (5-6)
Sum, (7-10)
Gene. (11-15)
Ver. (16-18)
LLaMA-3,1-SD-IT
27.12:42.53
4..74:1.55
64.42:00.55
65.78+1.71
76.25++1.27
56.63=1.43
LLaMA-31-SB-SPT
31.27:22.90
44.34:1.48
18.28:1
77.62+
80.59:1.08
64.27=1.70
+7.64
Gemma-3-12B
42.96:122.9
6.261
71.79=0005
77.34:1 ,44
82.51=11006 1.06
68.47=1.35
Gemma-3-128-SF
43.14442.86
1,25
84,19+11119
78,57++1.53
82.48+ 1.05
+3.12
Gemmaa-3-278
43.60++2.83
67.82++142
74.55=00.05
14.8
84.29:10.98
71.40=1.28
Cemma---27B-SSF
45.39:12.90
1.34
86,36=1.00
81.53++1.60
84.0611.96
73.43=1.64
+2.03
DeepScck-V3
39.54443.06
79.35+1..28
78.97=0005
86.23+11.19
85.48::0885
73.35=1.33
GPT-44
52.174:3.00
77.17+1.32
74.23++0.6
76.39++1.28
85.45+0..2
72.12-1.32
17B-128E, (12) A.AMMA--33-70--nsttrue and (13) -.LaMA--..--B-IIstteee For each setting, we conduct 1,000 1,000-iteration bootstrap significance tests (seed = 42) and report the mean and standard deviation. We summarize average performance scores across the Sci2Pol-Taxonomy in Table 2 and visualize the performance of open-source and commercial models in Figure 3 Appendix G.2 [.2provvdes provides full results for all 18 tasks, and see Appendix G.T for the detailed experimental settings.
and
We include further analysis in Appendix H covering: (1) human baseline (Appendix H.2 (2)
common failure modes (Appendix [H.3) (3) what SFT learns (Appendix [H.4): (4) the impact of prompt length (Appendix H.5); (5) the reliability of the Gemini-based judge (Appendix H. (6) over-
endorse analysis in Tasks 16 and 18 (Appendix H.8) ; (7) information leakage testing of in-context
polishing (Appendix(H.9)) (8) potential circularity in benchmark construction (Appendix 1oppendixH.10)... (9)
saturation analysis (Appendix H.IT); (10) trade-offs in generating briefs from abstracts, introductions,
or full papers (Appendix H.12
4.2 SUPERVISED PINE-TUNING ON Sc12POL-CORPUS
We validate the effectiveness of Sci2Pol-Corpus by fine-tuning three models: (1) LLaMA-3.1- 8B-Instruct, (2) Gemma-3-12B5 and (3) Gemma-3-27B. shows the results. All fine-tuned models show consistent improvements across Cci2Pol-Benchh Notably, fine-tuned Gemma-3 1amma-3--275 outperforms both GPT-4o and DeepSeek- V3 (671B), despite their significantly larger scale or broader capabilities. These findings highlight the value of domain-specific supervision in Sci2Pol-Corpus. indeed captures policy-relevant reasoning. See Appendix G.T for the experimental details. The cross-domain generality of our SFT methodology is further shown in Appendix [
CONCLUSION
We present Sci2Pol-Bence -Bench and Sci2Pol-Corpus, the first benchmark and training dataset designed specifically for evaluating and improving large language models on scientific-to-polic brief genera- tion. Grounded in five-stage taxonomy that mirrors the human writing process (Autocompletion, Understanding. Summarization, Generation, and Verification), Sci2Pol-Bende offers fine-grained evaluation framework spanning tasks. Our results show that even state-of-the-ee L.LMs struggle with factual grounding, actionability, and volicy-approprics reasoning, and that commonly used metrics such as ROUGE and BERTScore fail to capture brief-writing quality.
To address these limitations, curate Sci2Pol-Coruus from 5.6 million policy documents, producing 639 high-quality prier-brief pairs through citation- retrieval, LLM-assisted filtering, and expert- guided in-context polishing. Fine-tuning this corpus yields consistent gains across Gei2Pol-Benchl Notably, a fine-tuned Gemma-277 model surpasses much larger commercial systems, demonstrating that targeted, domain-specific supervision can outweigh scale for scientific-to-policy translation.
Together, these resources establish foundational infrastructure for studying and improving how language models translate scientific evidence into actionable policy guidance. We hope Sci2Pol- Bench and Sci2Pol-Corpus will catalyze future research toward models that not only understand science but can responsibly and effectively inform policy in high-stakes societal domains. We also provide detailed LLM usage in Appendix A dataset links in Appendix B limitations, future work, and broader impacts Appendix D and related works in Appendix
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REPRODUCIBILITY STATEMENT
We have made efforts to ensure the reprodueibility of our work. The full details of our benchmark construction, dataset curation, and model evaluation provided in the main text and appendices. For all 18 tasks in Sci2Pol- Rei2Pol-Bench, we describe the data sources, annotation protocols, and evaluation metrics in detail (Section 2). The construction steps of Sci2Pol-Corpus, including citation-based retrieval, LLM-based filtering, and in-context polishing, are documented in Section E We release all prompt templates and scoring rubrics in Appendix [ To facilitate replication, include the code in the supplementary materials. For each supervised fine-tuning experiment, we specify training configurations and hardware setup in Appendix [.I]] Collectively, these resources ensure that our results can be verified and extended by the community.
ETHIC STATEMENT
Human-in Comman-in-teed in-the-loop Annotation Process. All reviewers involved in task development and verifica- tion are postdoctoral researchers or research faculty in political science. They are co-authors of this paper and serve domain experts throughout the benchmark and dataset construction. No monetary compensation is provided; their participation is motivated by scholarly collaboration and shared commitment to advancing evidence-based policymaking.
Details of Human Involvement in Label Generation. Three tasks (5, 16, and 18) use GPT-o3 to propose initial labeled items, and all of them undergo full human verification. Each task is reviewed independently by two reviewers, with 100% reviewer agreement across all accepted items. For all 1, 1,200 samples in Task 5, no corrections are required. For Task 16, reviewers examine all 850 samples and correct three mislabeled cases (correction rate z 0.35%). For Task 18, reviewers validate all 706 generated contradictory implications, and no corrections are needed.
Copyright and Use of Scientific Papers. The benchmark and dataset are built from publicly accessible content such as article metadata. We release only article DOIs, metadata, access dates, policy brief metadata, and our annotations. The benchmark and dataset are released under CC-BY-NC 4.0, with two-tier protocol (open metadata + controlled transformed excerpts), and no publisher- owned text is redistributed. Derived tasks (e.g., summarization in Sci2Pol-Benche rely solely on reformulated excerpts for non-commercial academic purposes.
We also document potential impacts and implement explicit safeguards: all tasks require evidence- based summarization (not prescriptive advice), prompts prohibit unsupported claims, and evaluation judges penalize fabricated, speculative, or over-confident policy recommendations. To reduce evaluation variance and bias, we employ rubric-anchored scoring, multi-model judging, and expert- labeled calibration samples. These measures establish clear licensing boundaries, release protocols, bias-mitigation procedures, and risk-assessment practices appropriate for policy-oriented datasets.
For transparency, we provide detailed list of all used paper-brief pairs in Appendix DOIs, sources, and access dates.
including
ACKNOWLEDGMENTS
WW would like thank Robin Luo, Yichi Zhang, Qineng Wang, and Kongdi Zheng for enlightening discussions on related topies.
We acknowledge grant support from the National Science Foundation (award No. 2404035).
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Supplementary Material
LLM Usage Acknowledgement
29
Dataset Links
29
Prelininaries
30
Policy Brief
30
Comparison with Full Scientific Paper, Paper Introduction and Abstract
30
Limitations, Future Work, and Broader Impact
31
Limitations and Future Work
D.2 Broader Impact
32
Related Works
33
Scientific and Political Benchmarks
33
Scientific and Political Datasets
33
How LLMs Fail in Policy Brief Generation
34
F11
Contextual Depth
34
Hallucination Risk
35
................................
Readability and Tone
36
Actionability
Experimental Details
38
Detailed Experimental Settings
LLM Performance on Sci2Pol-Bench
39
G.3
Supervised Fine-tuning on Sci2Pol-Corpus
40
Additional Experimental Analysis
Limitations of BERTScore and ROUGE Scores for Tasks 11-15
41
H.2
Human Baseline for ScinPol-Bench
43
H.3
Analysis of Common Failure Modes
44
Analysis of Improvements from SFT
45
H.5
Impact of Prompt Length for Tasks 1-4
49
H.6
Human LLM Judge Agreement Evaluation
50
H.6.1
Direct Human Scoring
50
H.6.2
Confusion-matrix Validation
54
H.7
Why Tasks 11-14 Beyond Task 15: Section vs. Full Brief Generation
55
H.8
Over-endorsement Analysis on Tasks 16 and 18
58
H.9
Information Leakage Check of In-context Polishing in Section 3.3
59
H.9.1
Excluding Three Samples from Justification
59
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H.9.2
In-Context Polishing with Three New Published Pairs
59
H.9.3
Evaluating on New Eight Test Samples
60
H.10 Athlete as Judge Testing: GPT Family
61
H.10.1 Potential Circularity: GPT vs. DeepSeek Family
H.12 Model Substitution by MiniMax-M2
H.
Saturation Testing
62
12 Comparison of Writing from an Abstract, an Introduction, and Full Paper
63
Cross-Domain Generalization Testing
66
Details of 85 Expert-written Paper-brief Pairs
77
U.1 List of 85 Expert-written Paper-briel Pairs
77
J.2 Publication Year Distribution of 85 Policy Briefs
80
J.3 List of 8 New wxptrt Paper-brief Pairs
80
0.4 List of New In-context Exptrt-written Paper-briel Pairs
................................
80
Examples for Tasks 1-18
82
Prompts for ScinPol-Bench Evaluation and Dataset Curation
94
Tasks 7-10 Prompt for Reference-free Score
94
Task 11 Prompt for Reference- based Score
95
Task 12 Prompt for Reference-based Score
97
Task 13 Prompt for Reference-basee Score
98
Task 14 Prompt for Reference-based Score
99
L.6
Task 15 Prompt for Reference-based Score
100
Task 5 Prompt for Data Curation
101
L.8
Task 11 Prompt for Data Curation
102
Task 13 Prompt for Data Curation
103
10
Task 16 Prompt for Data Curation
104
Task 18 Prompt for Data Curation
105
M Prompts for Sci2Pol-Corpi Curation
106
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A LLM USAGE ACKNOWLEDGEMENT
Large Language Models (LLMs) were used in this project as general-purpose assistive tools across several stages of data curation, benchmark design, evaluation, and paper writing. We detail their precise contributions below.
We used LLMs (e.g., GPT-4o) to improve the elarity, grammar, and fluency of English writing throughout the paper.
Manuseript Editing:
SciZPol-Bench: (i) Data Augmentation and Annotation: For Understanding, Generation, and Verification tasks, GPT-o3 was used to generate training and evaluation samples, including sentence classification data, missing brief sections (e.g., Policy Problem and Study Methods for JHSB samples), and supported/contradictedd claims for claim verification tasks. (ii) Prompt-Baa Based Evaluation: Gemini-2.5-Pro was used as LLM-as-a-judee to evaluate model outputs for Tasks 7-15 based on clarity, factual accuracy, completeness, and policy relevance. (iii) LLM Evaluation: We benchmarked 13 commercial and open-source L.MSs.
SeiZPol-Corpus: (i) Filtering and Evaluation of Candidate Pairs: We employed GPT-o3 to identify 639 high-quality science-policy pairs from 140,000 candidates. (ii) In- In-Context Polishing of Policy Briefs: We used GPT-o3 with three expert-written examples in an in-context setup to revise raw policy documents into standard, structured policy briefs.
All LLMggenerated content used in data or evaluation workflows was curated or reviewed by human experts. We take full responsibility for all content in this paper, including that generated evaluated by LLMs. No LLM is credited as an author.
B DATASET LINKS
We provide the links for the datasets:
Seichol-Bench Dataset:
Sci2Pol-Corpus
Dataset:
Sci2Pol-Corpus
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C PRELIMINARIES
In this section, we present preliminaries on the policy brief (Appendix and compare it with the full scientific paper, paper introduction, and paper abstract (Appendix
C.I POLICY BRIEF
The policy brief is first introduced by Nature Energy provide concise, poliey-relevant summaries written by the original researchers. Each brief cites its source article using clear metadata (e.g.. "based on: title doi"), enabling reliable paper-brief alignment. It includes five parts:
Title: A concise one-line headline that states the policy issue.
Policy Problem: A short paragraph (fewer than five sentences) framing the socictal risk.
Scientific Research Findings: One or two compact paragraphs (about 150 words) clearly summarizing the study's 's core quantitative results and key empirical insights.
Scientific Research Study Methods: A single paragraph (about 100 words) briefly explaining the dataset and modelling approach in clear, accessible, and lay terms.
Policy Implications: Four to six bullet points (about 25 words each), drawn strictly from the paper's authors, highlighting concrete conclusions directly relevant to policymakers. No added recommendations, speculation, external examples.
C.2 COMPARISON WITH FULL SCIENTIFIC PAPER, PAPER INTRODUCTION AND ABSTRACT
We provide comparison between the policy brief and the full paper, introduction, and abstract.
research paper provides full evidence and methods for experts; its introduction frames the knowledge gap and aims; the abstract compresses aims, methods, and key results. A policy brief removes technical detail and distills only policy-relevant insights for decision makers.
Purpose:
Structure: Papers follow IMRaD (Introduction, Methods, Results, Discussion) with references; abstracts compress these into one paragraph. Policy briefs replace IMRaD with Policy Problem, Scientific Research Findings, Scientific Research Study Methods, and Policy Implications.
Tone and Jargon: Papers, introductions. and abstracts use technical language and equations. policy brief avoids jargon and equations, relying on plain prose accessible to non-experts.
Focus: Full papers emphasize evidence and methodological rigor; introductions stress scholarly significance; abstracts distill what is done and found. Policy briefs highlight why findings matter for policy, and repeat only the author's stated implications.
Audience Takeaway: Researchers consult full papers for replicable detail, introductions for rationale, and abstracts for quick overview. Policymakers rely briefs to grasp the problem, key evidence, methodological credibility, and policy implications.
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D LIMITATIONS, FUTURE WORK, AND BROADER IMPACT
We present the limitations and future work (Appendix D.I. and the broader impact (Appendix D.2)
D.1 LIMITATIONS AND FUTURE WORK
We have the following limitations and related future work:
ScicPol-Bench and Sei2Pol-Corpus remain modest in size. Unlike efforts that rely synthetic summaries or crowd-sourced approximations, our datasets reflect the communicative intent and expertise of scientists writing for policy audiences. We will continue to incorporate newly published brief's and their corresponding papers to expand coverage.
Although supervised tinettuning on Sci2Pol-Corpus yields consistent gains, does not yet surpass statc-of-the -ha----ffhhh-rttt art commercial models, c.g., Grok. Future work should explore improved training strategies and better use of Sci2Pol-Corpis to close this gap.
The existing 85 published policy briefs come from Nature Energy. Nature Climate Change, Nature Cities, Nature Sustainability, and the Journal of Health and Social Behavior (JHSB). Expanding Sci2Pol- and Sci2Pol-Corpus to additional disciplines and languages will enable broader generalization as new paper-brief pairs appear.
20 samples in Tasks 11 and 13 are drawn from the Journal of Health and Social Behavior (JHSB). For these cases, the Policy Problem and Study Methods sections are revised by GPT-o3 based on the Research Problem & Data section. This step may not be reliable as for the remaining 65 samples. However, our experts review them, and we chose to retain these cases because they account for more than 20% of the total sample set.
While Sci2Pol-Bend is intentionally designed around single-paper policy briefs, this focus also imposes inherent limitation. Our benchmark does not capture the broader genre of multi- source policy synthesis, where policymakers integrate evidence from many studies. As a result, benchmark does not assess, nor does our dataset enhance, model's capacity aggregate heterogeneous sources or integrate multiple lines of evidence.
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D.2 BROADER IMPACT
This work advances understanding of the critical connection between scientific research and policy- making. Science provides evidence and authoritative knowledge essential for informed decisions and sustaining public trust. Its role grows as pressing challenges such as climate change, public health crises, and rapid technological change demand the timely integration of new findings.
Sci2Pol-Bench -Brhch directly addresses a persistent bottleneck in this pipeline: policymakers' difficulty in translating dense, nuanced scientific evidence into actionable guidance. By decomposing the brief writing process into five stages and providing 18 fine-grained tasks, it offers the first systematic evaluation framework tailored to scientifictto-poliyy communication. This benchmark exposes weaknesses in even frontier language models and creates opportunities to build models that are not only accurate but also clear, consistent, and persuasive for policy audiences.
Sci2Pol-Corpe complements this evaluation framework by supplying the first domain-speciffe training dataset for policy brief generation. Curated from millions of policy documents and refined through quality filtering and in-context revision, it provides 639 high-quality papers to brief pairs. Fine-tuning on this corpus leads to consistent gains across Eci2Pol-Benche even enabling smaller open source models to surpass frontier-scale models. This demonstrates the importance of targeted supervision for capturing the communicative intent scientific-to-polie translation.
Together, these resources have significant and lasting societal impact worldwide. They empower scientists, policymakers, and institutional leaders with practical tools to measure and improve the policy relevance of scientific communication. They strengthen accountability and reduce serious risks of misinformation by encouraging accurate, structured, and transparent communication of evidence. They also highlight the vital and growing role of science in shaping societal outcomes and help ensure that policy decisions remain grounded in rigorous research rather than speculation.
At time of political polarization, resource constraints, and global competition, the ability generate clear, accurate, and actionable policy briefs is vital. Sci2Pol-Bench and Sci2Pol-Corpus provide foundational infrastructure for developing models that meet this challenge, supporting evidence-based Decision-making and enhancing public trust in science and governance.
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E RELATED WORKS
We review related work on benchmarks and datasets in the scientific and political domains.
E SCIENTIFIC AND POLITICAL BENCHMARKS
In recent years, LLMs (Team et al. 2024, Liu et al. 2024a Touvron et al. 2023 Achiam et al. 2023] Bai et al. 2023) have attracted significant attention due to their impressive performance. There have been several benchmarks to evaluate their performance in scientific and political domains.
Scientific Benchmarks. In the scientific domain, benchmarks such as SciRIFF (Wadden et al. 2024 MMLU-Pro (Wang et al. 2024). Scilnstruct (Zhang et al. 2024a), SciLitLLM (Li et al. (024c). and Seil (Singh et al. 2023) test models on tasks like summarization, question answering, and claim verification. These benchmarks focus on instruction- eestructionfoolloinng and comprehension of scientific content. Most use single-step tasks with scientific inputs, typically full papers or extended passages.
Political Benchmarks. In the political domain, benchmarks assess how models reason about political science, ideology, and value aligmment. Political Science QA (Li al. (024b) tests factual knowledge and reasoning. Röttger et al. (2024) and and[Ren et al. (2024) probe value orientation and political opinions. MMtoki et al. (2024) measure political bias. These tasks often use short prompts under 300 tokens and rely on multiple-choice formats. Political-LLM (Li et al. 2024b) contributes useful taxonomy and discussion of use cases, but offers no benchmark or annotated data.
However, existing scientific and political benchmarks overlook the dual challenge of -cience-informed policy communication: understanding complex research and translating it into actionable language. SciRIFF and Scilnstruct focus on cnttuucttoo-oolloiiiii for scientific tasks, while MMLU-Pro tests expert-level reasoning with multiple-choice questions. Political benchmarks such as ValueBench or Political-LILM probe ideology and values but do not address policy generation or scientific grounding. To fill this gap, Cci2Pol-Bench evaluates LLMs on generating policy briefs from full-length scientific papers, pairing them with expert-written brief's and introducing five-stage pipeline. We compare against three representative efforts. SciRIFF and MMLU-Pre -Pro are the closest scientific benchmarks: SciRIFF covers single-step tasks like summarization, QA, and claim verification, while MMLU-P measures reasoning breadth but not policy translation. Political-LLMM provides the first principled framework for computational political science, offering a taxonomy but no annotated tasks. Together, these works form the strongest prior art in scientific comprehension and political reasoning. Sci2Pol- Bench builds on them but uniquely combines scientific fidelity with direct policy relevance, making it the first benchmark to evaluate LLMs on producing accurate, actionable policy briefs.
E.2 SCIENTIFIC AND POLITICAL DATASETS
Beyond evaluation benchmarks, several open-source fine-tuning datasets adapt LLMs scientific and political domains through supervised instruction tuning or continued pre-training.
Scientific Datasets. Researchers have curated domain-specifie corpora to enhance scientific reason- ing. Scilnstruct (Zhang et al. 2024a) provides curated instructions across physics, chemistry, math, and formal proofs, improving models' performance on college-level problems. SciLitLLM (Li et al., 2024c) combines continual pre-training on research papers with supervised tuning. It introduces SciLitins, collection of instructions targeting underrepresented fields. Large-scale corpora have also been used. For example, Galactica (Taylor et al. 2022) is trained on 48 million science documents. This shows how domain-specific training endows models with broad scientific knowledge. Together, these datasets demonstrate the effectiveness of specialized fine-tuning.
Political Datasets. In the political domain, fine-tuning datasets emphasizes factual knowledge, ideology, explicit opinions, and bias alignment. PoliTune (Agiza et al.) 2024) creates ideology- specific instruction data from social media, while Vendetti et al. (2025) fine-tune the existing LLMs on curated Reddit discourse to model polarized political commentary. These efforts show how targeted fine-tuning can steer models toward particular political knowledge or stances.
However, existing datasets in both domains overlook the dual challenge of communication. Scientific datasets like Scilnstruct and SciLitIns strengthen technical reasoning but exclude crucial policy text, while political datasets such PoliTune emphasize ideological alignment yet ignore underlying scientific evidence. Sci2Pol-Corpus fills this important gap by linking full-length research papers expert-written policy briefs. It trains advanced models to extract key findings and translate them into clear, actionable recommendations for diverse policymakers.
policy
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F HOw LLMS FAIL IN POLICY BRIEF GENERATION
In this section, we present representative examples that highlight the limitations of existing LLMs in ecientific-to-polii brief generation. Using the prompt in Table 59
instruct different L.MSs to generate policy briefs from the scientific papers listed in Appendix J| and then compare their outputs with the expert-written published briefs. The results reveal issues in four key areas: contextual depth, hallucination risk, readability and tone, and actionability. We provide one illustrative example for each issue in Appendix F.I
Appendix
F:2 Appendix and Appendix
F.4
F.1 CONTEXTUAL DEPTH
Table is an example where LLM output contains a contextual-depth issue. The issue is evident because the published brief gives precise quantitative findings such as "an additional 0.5 GtCOz/yr by 2030 and 1.9 GrCOz/yr by 2050" and compares these against Paris Agreement scenarios to highlight the "CDR gap". However, the LLM output only provides a broad summary about reliance on based removals and a lack of novel technologies. does not capture the specific numerical evidence and scenario-bare based reasoning that are central the paper's findings.
Table 4: Expert- LLM-written Policy Brief Excerpts: Contextual Depth Issue. Paper: The carbon dioxide removal gap (Lamb et al. 2024a). LLM: GPT-4o. Presented Brief Section: Scientific Research Findings.
Expert-written Brief
LLM Output
In our study we found that compared with 2020, the most ambitious national proposals for CDR imply an additional 0.5 GiCO2yr-1 of removals by 2030, and 1.9 GICO2yr- by 2050. Compared with CDR scaling in Paris found
Research indicates that current national pro- posals for CDR are not aligned with inte- grated assessment model scenarios, which project the levels necessary to achieve the 1.5%C temperature limit. The study shows that while afforestation and land-based moval methods are in use, there is lack of commitment scaling novel CDR tech- nologies like bioenergy carbon capture and storage (BECCS) or direct air carbon capture and storage (DACCS). Most proposed CDR efforts remain heavily reliant on hand-based removals, which are not sufficient on their own to meet the required targets.
Agreement-consistem scenarios, that these national CDR proposals tend to fall short by hundreds of megatonnes of carbon dioxide in 2030 to several gigatonnes of car- bon dioxide in 2050, highlighting 'CDR gap'. However, we find that the most ambi- tious proposals do come close to levels in a low-energy- demadd scenario where CDR re- quirements are minimized, suggesting that if countries pledge more ambitious emissions reductions consistent with these scenarios, the CDR gap will be closed. As levels of report- ing vary, our evaluation of proposed CDR does assume that number of countries sim- ply maintain their current levels of (conven- tional) removals. In addition, remains un- known to what extent firm CDR policies will follow these proposals.
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F.2 HALLUCINATION RISK
T shows risk of hallucination. this case, the LLM reads the paper as endorsing public or cooperative ownership, progressive swaps, cross-border subsidies, and profit-cap rules. The paper does not state these. The human brief stays close to the source and lists the core points: trillion-dollar scale, windfall profits under unregulated markets, trade-off' from profit caps, regional distribution effects, and the near-term need to scale NETs. The LLM adds new policies and causal claims, so it may mislead readers.
Table Expert- vs. LLM-written Policy Brief Excerpis: Hallucination Issue. Paper: Inqqulitty
repercussions of financing negative emissions (Andreomi et al. 2024b). LLM: Qwen3-8B. Presented Brief Section: Policy Implication.
Expert-written Brief
LLM Output
In a net-zero emissions world, NETs could become trillion-dollar business globally.
1. Equitable Ownership Structures: Policy- makers should consider public ownership cooperative frameworks for NETs to prevent disproportionate benefits to pri- vate capital holders. This could mitigate the ownership effect and ensure that prof- its from carbon removal are distributed more fairly.
If financed through an unregulated carbon market, the owners of these companies would enjoy windfall profits, potentially leading to large increase in economic inequality.
2. Progressive Taxation and Redistribution: Replacing regressive carbon taxes with progressive income taxes after net-zero, or using redistribution mechanisms, could counteract inequality risks. Pre-net-zero taxation reforms might also reduce ad- verse impacts, though they could compro- mise economic growth.
Market regulation, such as profit caps, could reduce the inequality increase, but at the risk of stimulating too much too little carbon removal policymakers should be aware of this trade-off.
Concentrating removal efforts in the Global North transferring resources to the Global South could, to some ex- tent, offset the increase in inequality at the global level.
International Financial Transfers: Im- plementing cross-border carbon removal credits or subsidies for low-income coun- tries could address global inequality. However, these transfers may still ben- efit high-income groups within recipient nations, necessitating targeted redistribu- tion policies.
These dynamics mostly apply a net- zero and post-net-zero world. The current priority of policymakers should remain to provide adequate resources to scale up NETs towards technology maturity.
4. Regulating Profit Margins: Introducing profit caps or alternative pricing schemes for NETs within single carbon market could balance incentivizing deployment with equity. Yet, such measures must avoid distorting markets or discouraging investment.
5. Integrated Policy Design:
Climate strate- gies must explicitly account for distribu- tional impacts in policy frameworks. This includes designing carbon pricing, owner- ship rules, and revenue allocation mech- anisms align with equity goals while maintaining the viability of NETs as part of low-carbon transition.
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F.3 READABILITY AND TONE
6 is an example where the LLM output has readability and tone issues. The expert- brief active voice, and speaks policymakers. It defines the study design, maps three strategies to plain descriptions, and explains how the team built and compared scenarios. By contrast, the LLM output contains unexplained jargon bullet points. also introduces acronyms without setup, which raises the barrier for non-expert readers.
logically ordered,
Table 6: Expert- vs. LLM-written Policy Brief Excerpts: Readability and Tone Issue. Paper: Demand-sidh strategies enable rapid and deep cuts in buildings and transport emissions 2050 (van Heerden et al. (025a). LLM: DeepSeek- V3. Presented Brief Section: Scientific Research Study Methods.
Expert-written Brief
LLM Output
Our study uses quantitative scenarios gener- ated with global integrated assessment mod- els (IAMs) that are specifically designed to capture the interactions within and between the energy system, the economy, and the en- vironment. We developed three scenarios that each capture the impact of specific strategy targeting energy use. In the first scenario end users reduce or change energy-using activi- ties, the second they adopt more efficient technologies, and in the third they switch to clectrification or alternative energy carriers as substitutes to fossil fuels. Using a structured questionnaire, we also gathered inputs from policymakers and experts in demand sectors to refine the scenarios. We quantitatively in- terpreted and quantified the scenarios through 2050 using seven IAMs. Finally, we com- pared the resulting emission reductions per strategy across the set of models and scenarios and analysed the results to better understand the main factors driving these reductions.
The study used an ensemble seven global integrated assessment models (IAMs) sim- ulate three intervention strategies:
1. Activity-focused (ACT): Policies promot- ing shared mobility, reduced travel, and smaller living spaces.
2. Technology-optimizzi (TEC): Enhanced
building insulation, vehicle efficiency standards, and retrofits.
Flectrification-ocusse (ELE): Wide- spread adoption of heat pumps, electric vehicles, and renewable energy.
Scenarios were compared against "current policies" baseline and a C.lCgee path- way, accounting for regional variations and model uncertainties.
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F.4 ACTIONABILITY
Table 7 shows weak actionability. The LLM states broad aims: "reconsider approaches", "build public support", and "prioritize durable reforms". It does not say who does what, when, how to track success. The expert-written brief gives steps and targets: start with the most damaging fuels, especially coal: invest in transit and support EVs; use fuel-efficiency rules; and choose measures that survive political shocks. The LLM repeats goals but omits scope, sequence, and levers, so policymaker cannot act.
Table 7: Expert- vs. LLM-written Policy Brief Excerpts: Actionability Issue. Paper: Fossil fuel sub- sidy reforms have become more fragile (Mahdavi et al. (025a). LLM: Gemma3- 12B. Presented Brief Section: Policy Implication.
Expert-written Brief
LLM Output
The findings suggest that current strategies for reducing fossil fuel subsidies failing. Poli- cymakers should reconsider their approaches, prioritizing:
While fossil fuel subsidies are harmful, efforts to remove them fail more than 90% of the time. Low-cost fuel is popular and hard change.
Long-Term Strategies: Focus on policies that reduce demand for subsidized fuel (e.g., fuel efficiency standards, electric ve- hicle incentives, improved public transit) alongside, rather than in place of, subsidy reductions.
Reforms are only beneficial if they en- dure. While price reforms are fragile, other kinds of policies such as invest- ments clean transportation and support for electric vehicles are more durable.
Since subsidies are politically difficult to remove, governments should concentrate
Political Feasibility: Recognize the politi- cal challenges of subsidy reform and build public support through clear communica- tion, targeted compensation for vulnera- ble populations, and gradual, phased approach.
their efforts on the most damaging types of fuel, especially coal.
Governments should look for alternative ways to reduce the demand for subsidized fuel, for example, by using regulatory measures enhance fuel efficiency, in- vesting in public transit and promoting electric vehicles.
Prioritize reforms like shifting to floating prices that are not eas- ily croded by inflationary pressures or ex- change rate fluctuations, even if initially less popular.
Durable Reforms:
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G EXPERIMENTAL DETAILS
We provide: (i) the detailed experimental settings for LLM evaluation on Sci2Pol-Bench and super- vised fine-tuning (Appendix G.T): (ii) the performance of 13 LLMS across all Sci2Pol-Bench tasks (Appendix G.2) ; and (iii) the supervised fine-tuning results of three L.LMs (Appendix G.3).
G.1 DETAILED EXPERIMENTAL SETTINGS
We present the detailed experimental settings.
Evaluate LLMs on SciZPol-Bench. We evaluate 13 LLMs through API calls. For Grok, GPT-4o, and Claude, we use their respective private APIs, For all other models, we rely on the Novita API.
Supervised Fine-tuning. Our supervised fine-tuning uses LLaMA-Factory (Zheng al. 2024 We apply lightweight Low-rank Adaptation (LoRA) (Shen et al. 2022) under the following setup. The base models are LLaMA-3.1
Gemma-3-12B-I1 and Gemma-3-27B-I LoRA uses rank 8, alpha 32, and dropout 0.05, applied to the query and value projection matrices of all attention layers. Training runs for six epochs with batch size 8, cosine learning rate schedule, peak learning rate of le-4, and a 5% warmup phase. FlashAttention
(Dao 2024) and the Liger kernel (Hsu et al. 2025) are enabled for efficient long-context training. We split Sci2Pol-Corpus into 95% training and 5% validation, and select the checkpoint with the lowest validation loss. We run all experiments on NVIDIA A100 80GB Tensor Core GPUs.
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G.2 LLM PERFORMANCE ON SCI2POL-BENCH
We show the detailed performance of 13 LLMs across all tasks in Table 8 Our experiments lead to three main findings:
Current LLMs struggle to generate high-quality policy briefs from scientific papers, even though they likely encounter academic content during pretraining. These results reveal a persistent gap between general language capability and task-specific policy reasoning.
policy
Sci2Pol-Bench exposes weaknesses not captured by traditional LLM benchmarks. As shown Table 2 and Table tasks in Autocompletion and Understanding produce the widest FI range across models, highlighting instability in predicting coherent and grounded continuations.
Larger models generally perform better, but size alone doesn't 't guarantee robustness, For instance. Gemma-3-12B outperforms the larger Gemma-3-27B on several tasks, e.g., Task 2 (see Table and Table Sp. Similarly, commercial models that dominate benchmarks like GPQA (Phan et al. 2025 often underperform on early-stage Sci2Pol tasks such Task and Task 4.
Table 8: Detailed Performance LLMs on SciZPol-Bench.
Autocompletion
Understanding
Summarization
Model
(F1)
T2 (F1)
(F1)
(F1)
T6 (F1)
(Gem.)
Grok-3-beta
71.05+22.77
63.76+2.79
33.12+22.92
35.15-43.08
90.42:0.80
69.83=1.66
83.45:+0.05
DeepSeek-R1
47.92:22.11
44.42+2883
40.45
46.26:13.18
89.83:10.78
83.39-1.29
82.91-10.04
Qwen3-235B
53.1442..5
51.75+2.81
39.644+.200
44.33443.16
87.81+0.76
77.48+0.15
D3epSeek-V3
41.62++2.90
38.3913.26
39.9111..28
38.23:12.79
88.99:10884
69.72-1.72
80.30:10.05
GPT-40
73.7442.81
63.334-2882
31.59+4..77
40.01443.009
90.56++0.80
63.77-1.84
75.63++0.06
Claude-37-Somnet
60.13:12..3
44.08:3.22
34.38:12..93
37.67:13.27
93.72:10.73
66.41=1.65
83.38:10.05
Gemma-3-278
64.5243.11
58.80+22.74
25.56++2.885
25.51442..3
82.07 1.09
53.57-
77.61+00.5
Mistral-Large
62.46122.81
55.0113.001
29.6411.38
29.25::2.46
89.45:10.82
63.08=1.65
81.90:10.05
I.AMA-3.3-70B-IT
72.51++2.35
7..99
32.26-12.72
36.19413.14
87.62
60.66
75.15
LLaMA-4-Maverick
55.4113.04
43.824-277
25.27:42.88
30.46:12.90
86. 10:10.82
81.52=1.21
75.48:10.06
Gemma-3-12B
64.18:13.02
60.111:3.18
25.47:12.59
22.09-12.39
88.19:10888
51.04
73.81
Qwen3-8B
33.49=2.86
32.46=2.70
33.76:13.00
40.90:122995
87.24:10.9
1.44
75.80:10.17
LLaMA-3.1-8B-IT
37.53-2.92
2.408
16.441241
22.44:12.30
55.08:1
40.41 1.66
67.38:10.05
Summarization
Generation
Model
T8 (Gem.)
T9 (Gem.)
T10 (Gem.)
T11 (Gem.)
T12 (Gem.)
T13 (Gem.)
T14 (Gem.)
Crok-3-beta
86.88=0.05
83.55:00.05
79.15=0005
92.35:10.63
79.5011088
82.67:11.40
89.38: 1.06
DeepSeek-R1
86.30=0.04
76.73110.0
77.36=0.04
92.77-10.74
19.1011.98
88.02+1.522
84.64.1 1.44
Qwen3-235B
80.38=0.15
74.63=0.15
75.60=0.15
93.551:0.55
17.74
10.40:11466
86.67=112
V3
80.95-0.05
76.90+0.005
71.72-0.05
91.50++0.3
78.24+1.46
90.29+00.99
87.62++ 1.28
GPTT-40
7.020=0.06
71.38:10.06
74.70=0.06
89.17:10.98
72.6911.05
72.81:1.500
78.36:
Claude-3.7-Sonnee
86.38=0.05
77.85:0.05
83.23=0.05
81.29:13.37
66.62.43.34
68.14
77.08=3.58
Gemma-3-277
75.98=0005
68.57-10.05
76.03=0.05
91.33-+-.87
75.23++1.12
88 12::1.2233
88.54=11244
Mistral-Large
79,90=0.05
74.75100.05
71.72=0.05
87.6410.92
73.02++1.06
1..1
77.53= 1.88
L.L.MA3333-00--IT
71.85=0.06
66.15-+0.06
71.75-0006
81.40-1144
71.55++1.19
60.02+-2.07
70.86+2.01
-.aaMA-4-Maverccck
75.48=0.06
70.60:0.006
68.33=0.06
82.37+41.59
74.52+0..5
62.45:11.74
1.55
Gemma-3-12B
71.18=0.05
65.45110.05
76.71=0.05
85.6011102
10.22211.15
70.6912.29
84.46 1.29
Qwen3-8B
77.48=0.17
72.08-0.117
70.94=0.17
91,59-+11000
12.62-11.17
77.50=1..7
79,91=1.84
LLLMM-3.1-8B-I1
67.38-0.05
59.03
63.88=0.05
82.35 +1.55
64.62 1.65
55.12 1.90
67.74-
Generation
Verification
Model
T15 (Gem.)
T16 (F1)
T17 (F1))
T18 (1'1)
Average
Rank
Grok-3-beta
89,58-0.94
98.60-0.42
59.26::1.67
98.48-0..0
77.01
1
DeepSeek-R1
19.23=:1.63
94.55=0.86
19.74:1161
97.23:10.69
75.05
2
Qwen3-233B
1.56
95.3610.73
58.51111.5
97.4110.00
1.34
DeepSeek-V3
83.51=1166
98.97-0.37
59.5241.61
97.9410.57
13.3
GPT-40
68.93-1.31
98.61:00.99
60.25:11.47
97.49:10.59
12.12-11.32
5
Gemma-3-27B
80.89++1226
98.2510046
59.88:11.52
94.74.10.95
71.0-11.28
Claude-3.7-Somnel
74.82+3.82
98.35-+0.47
$7.92-41.47
93.4
1.99
Mistral-Large
13.15=1131
95.57-00.66
59.22-11.56
90.831:1.12
70.23
I..MMA-33-70B-IT
65.60+1441
99.07+0.32
61.10+1.59
96.96+00.69
69.58+1.37
4.aMMe4-Maverick
72.86:11.11
96.94-1..1
57.99-L1.60
97.54-10.55
68.87-1.311
10
Qwen3-8B
6..32+11.69
92.26=0.78
$7.51+1144
95.85=0.82
68.51=1.39
Gemma-3-128
75.71+11.43
95.85+0.59
57.43+11.55
94.26+0.84
68.47+1.35
12
LLaMA-3.1-8B-IT
1.50
18.57+
54.40=t1.64
1.07
56.63=11.33
13
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G.3 SUPERVISED FINE-TUNING ON ScI2POL-CORPUS
We report the detailed supervised fine-tuning performance three LLMs in Table
Table 9: Detailed Performance of L.MSs with Supervised Fine-tuning (SFT) on Sci2Pol-Corpus.
Autocompletion
Understanding
Summarization
Model
T1 (F1)
T2 (FF)
T3
(F1)
TS (F1)
T6 (F1)
T7 (Gem.)
LLaMA-3.1-8B-IT
37.53=2.92
32.08-2.47
16.4412.41
22.44::2.30
1..08:1144
40.41=1.66
67.38:10.05
LLAMA-3.--B-SFT
38.8243.13
33.73+3.03
27.45:22.00
25.10:12.63
1.35
46.10=1.59
81.25:1:1.30
Gemma-3-128
64.18+3.02
60.11+3.18
25.47:22.59
22.09-4-2.39
88. 19-4-988.8
51.04=1.68
73.81+00005
Gemma-3-128-SFT
63.14:22.99
56.47:12.97
27.45::2.75
25.49:12.74
88.67:10.88
50.40 1.61
87.47:111.12
Gemma-3
64.3243.11
58.80+2.74
25.56+42.88
25.51442.63
$2.074 1.09
53.57=1.75
77.61+0.05
Cemma--3277B-BBB
69.02#2.98
25.29=2.92
27.45:22.82
29.80:22.86
80.08
54.80=1.59
89,30:1:1
V3
41.62:42.90
38.39
39.91113.28
38.23:12.79
88.99.10.84
69.72-1.77
80.30:10.05
GPT-4o
73.74=2.81
63.33+2.82
31,59:3.277
40.01:3.00
90.56:10880
63.77=1.84
75.63+:0.66
Summarization
Generation
Model
T8 (Gem.)
T9 (Gem.)
T10 (Gem.)
T11 (Gem.)
T12 (Gem.)
T13 (Gem.)
T14 (Gem.)
LLaMA-3.1-88-IT
67.38-0.05
59.03-+0.05
63.88=0.05
82.35 +1.55
64.62 +1.65
55.12 +1.90
67.74-+1 1.89
LLaMA-3.1-SB-SFT
71.17=1.45
1..00::.114
84.72=
16.66:1.111
71.10:1.47
1..1941.83
1..41=1.61
Gemma-3-12B
71.18=0.05
65.45:10.05
76.71=0005
$5.60++1.02
70.22::1.15
70.694...29
84.46 1.29
Gemma-3-12B-SFT
79.17=1.39
81.75::1.22
88.38 1.03
1.03
89.76:1:1000
71.93:t:1399
70.92:+2.08
84.18: 1.52
Gemma-3-277
75.98=0.05
68.57:10.05
76.03=0.05
91.33:10.87
75.23:1:.12
88.12::1.33
88.54:
Semma----77B-SP-
85.42-1.15
1.03
87.30
91.76++0.90
78.26
67.34++2.45
86.65+
DeepSeek-V3 V3
80.95=0.05
76.90110.05
77.72=0.05
91.50:10.92
18.24:11.46
90.29:10.99
87.62: 1.28
GPT-4o
75.20=0006
71.38:0006
74.70=0.06
89.17::0.98
72.69:11.05
72.81
78.36: 1.54
Generation
Verification
Model
T15 (Gem.)
T16 (F1)
T17 (F1)
TI8 (F1)
Average
Gain
L.LMA-331-8B-IT
19.05=1.555
88.57+ 1.08
54.40+1.66
85.79+1.000
56.63+1144
LLMMA-31180-SFT
72.76=1..5
93.29:10.84
53.2011.59
95.29:0088
64.27111.70
+7.64
Gemma-3-12B
75.71=1143
95.85:+0.59
17.43++1.75
94.26:2..84
68.47
Gemma-3-12B-SFT
76.06.11.66
97.18.10.55
57.70.11.56
92.57.11.03
1..59
+3.12
Gemma-3-277
80.89 1.26
08.25++0.46
1..88++1.52
94.74+00.5
71,40+ 1.28
Remma-3-27B-SFT
83.65
97.76:+0.51
60.00+1.54
94.43+0.84
1..43:1164
+2.03
DeepSeek-V.
83.51+1.65
98.97400.37
59.52++161
97.94+0.557
1.335+
GPT-40
68.9321131
98.6110039
60.25+1.47
97.49-10.59
72.12+1..2
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H ADDITIONAL EXPERIMENTAL ANALYSIS
In this section, we examine the following aspects in Sci2Pol-Bench and Sci2Pol-Corpus. (1) We provide evidence for the limitations of BERTScore and ROUGE when applied Tasks 11-15
(Appendix H.T.. (2) We provide the human baseline for Sci2Pol-Bence (Appendix (.2)). (3) We
provide the analysis of common failure modes of top LLMs (Appendix H.3 (4) We provide detailed analysis of what supervised fine-tuning learns (Appendix H.4)
(5) We test the impact of
prompt length performance for Tasks 1-4 (Appendix [..5). (6) We assess the reliability of the
Gemini-2.. -.5-Pro-tii base reference-free judge by alignwing with human evaluation (Appendix H. (7)
We contrast by-ttnnn versus full-brief generation explain the need for Tasks 11-14 in
addition to Task 15 (Appendix H.77 (8) We analyze whether models show tendency to over-endorse by studying the distribution of false positives and false negatives Tasks and 18 (Appendix (H.8).
(9) We validate that in-context polishing does not introduce information leakage (Appendix H.9
(10) We evaluate potential circularity in benchmark construction by comparing GPT and DeepSeck families Task 16 (Appendix (H.10.. (11) We do the saturation analysis (Appendix [H.11. (12)
We compare brief generation from abstracts, introductions, and full papers to analyze trade-offs in
context length (Appendix [2.1).. Together, these studies clarify the robustness and fairness of our Sci2Pol-Bench and Sci2Pol-Corpus.
H.1 LIMITATIONS OF BERTSCORE AND ROUGE SCORES FOR TASKS 11-15
We provide evidence for why BERTScore and ROUGE scores fail to evaluate Tasks 11-15.
policy brief for the scientific paper How Central Banks Address Climate and Transition Risks (Paper (Shears et al. 2025a): Brief (Shears) al., (0255)). For BERTScore, we demonstrate that deleting large portions of the candidate brief hardly changes the score against the true brief. Using Table 10 we define "brief completeness" by removing sections of the candidate brief to create 75%, 50%, 25%, and title-only variants. Table then reports the BERTScore trend as completeness falls.
Consider example:
For ROUGE scores, we show that simple grammatical or paraphrasing changes cause the scores to drop sharply, even when meaning is preserved. Table 12 presents example texts, and Table 13 reports ROUGE- 1/2/L. scores between them. The results reveal low scores despite semantic equivalence, highlighting ROUGE's sensitivity to surface form.
Together, these findings illustrate two failure modes: BERTScore remains high under major delctions, while ROUGE collapses under harmless paraphrases. Consequently, for Tasks 11-15, we rely on task-specific LLM-judge scores, which verify section coverage, reasoning flow, and evidence linkage.
Table 10: Deriving Completeness Subsets from LLM Outputs. Sections are removed sequentially in the order: Policy Implications + Methods - Findings - Policy Problem. The table indicates which sections remain each completeness level. Percentages reflect the proportion of content retained.
Completeness
Policy Problem Findings
Methods
Policy Implications
100% % (Full)
75 %
X
50 %
X X
x
x
25 %
X
Title Only
X
X
X
X
Table 11: BERTScore under Progressive Section Deletions from an LLM Brief. Scores remain high despite missing sections. See Table 10 for the definition of percentage levels.
Brief Completeness
BERT Precision
BERT Recall
BERT F1 Score
Full Brief
0.8689
0,8599
0.8644
75 %
0.8721
0,8612
0.8666
50 %
0.8829
0.8551
0.8688
25 %
0,8828
0,8174
0.8489
Title Only
0.8738
0.7839
0.8264
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Table 12: Original Grammar-alternated Policy Implications. We present two semantically equivalent
versions of the same "Policy Implications" section side by side: the left column is the original expert wording, and the right column rewrites sentences by alternating grammar/phrasing only (no change in meaning). This pairing is used to evaluate whether shallow changes alone depress ROUGE scores.
Original Text
Grammar-alternated (Rephrased) Text
Policy Implications
Policy Implications
Central banks vary substantially in the extent to which they re-risk stranded asset and physical climate risks and de-risk clean energy invest- ments.
Substantial variation exists across central banks in how they re-risk stranded asset and physical climate risks while de-risking clean energy in- vestments.
Central bank actions on climate risks are pos- itively associated with their country's climate policy stringency and public concern with cli- mate change and less with its underlying eco- nomic risks.
Their actions on climate risks are linked more strongly with national climate policy stringency and public concern over climate change, and less strongly with economic fundamentals.
National climate policy is not replaced by cen- tral banks, even with their autonomy; rather, is supported and complemented in advancing the clean energy transition.
Despite their autonomy, central banks do not
substitute for the lack of national climate pol-
icy but complement existing national policies promoting the clean energy transition.
The political character of central bank efforts to handle transition and physical risks raises concerns about unmanaged threats to the global economy, especially stranded asset risks.
The political nature of central bank actions to manage transition and physical risks raises con- cerns about unmanaged risks in the global econ- omy, specifically stranded asset risks.
Transparency of the risk-mitigation gap could improved by a central bank climate index, and international institutions governing central banks could set standards for climate and tran- sition risk management.
A central bank climate index could increase transparency of the risk mitigation gap; inter- national institutions governing central banks could set standards for climate and transition risk management.
Table 13: ROUGE Original vs. Grammar-alternated Paraphrase. We report ROUGE- 1/2/L scores between the two columns in Table [2] Despite identical meaning, scores are low. These illustrate ROUGE's sensitivity word order and phrasing rather than semantic equivalence.
Pair
ROUGE-1 F1
ROUGE-2 F1
ROUGE-L F1
Original vs. Rephrased
0.4058
0.1606
0.2319
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H.2 HUMAN BASELINE FOR SCI2POL-BENCH
We recruit two university-affiline affiliated evaluators and compensate each with $100. Because full evaluation time-intensivve, do not assess all questions. Instead, randomly sample 25 questions from each task in Tasks 1-10 and 16-18. For Tasks 1 11-15, we randomly sample three questions each, using the same three underlying scientific papers across all five tasks to maintain consistency. Due the limited number of evaluation questions, we report only the final results and do not conduct bootstrap significance testing. The resulting evaluations, together with the state-of-the-ar Grok-3-beta results, are shown in Table 14 These results indicate that current LLMs still have meaningful room for improvement.
Table Detailed Human Baseline Evaluation. We present the evaluation results obtained from two independent human evaluators.
Autocompletion
Understanding
Summarization
Model
T1 (F1)
T2 (F1)
(F1)
(F1)
(F1)
T6 (F1)
T7 (Gem.)
Evaluator 1
92.00
84.00
84.00
100.00
92.00
94.20
Evaluator 2
96.00
92.00
76.00
80.00
96.00
92.00
91.60
Grok-3-beta
71.05:12.77
63.7612.77
33.12.1.92
35.15.13.08
90.42.10.80
69.83.1..m 1.64
83.45.00.05
Summarization
Generation
Model
T8 (Gem.)
T9 (Gem.)
T10 (Gem.)
T11 (Gem.)
T12 (Gem.)
T13 (Gem.)
T14 (Gem.)
Evaluator
96.20
91.00
89.00
95.67
87.24
88.74
96.84
Evaluator
94.60
92.20
87.20
94.19
89.12
87.32
91.23
Grok-3-beta
86.88 +0005
83.55+0.00
79.15 +.005
92.35 +0.63
79.50 79.50++0.85 0.86
82.674 1.40
89.38+ 1.06
Generation
Verification
Model
T15 (Gem.)
T16 (FI)
T17 (F1)
T18 (F1)
Average
Evaluator 1
93.45
100.00
84.00
100.00
91.57
Evaluator 2
95.12
100.00
92.00
100.00
91.48
Grok-3-beta
89.58110.94 94
98. 08.60:10.42
98 98.48:10.50 59.26:11.67
1..01
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H.3 ANALYSIS OF COMMON FAILURE MODES
In this section, we summarize the most salient failure modes of strong general-purpor LLMs by task group and connect them our design targets.
Autocompletion (Tasks 1-4). On scientific and policy autocompletion and sentence reordering, leading models often rely on shallow lexical cues rather than discourse structure. For Tasks 1-2, models frequently choose continuations that share surface overlap (entities, topical words) but violate local rhetorical flow (e.g.. jumping to limitations policy implications prematurely). For Tasks 3-4, they can correctly identify the "introductory" sentence but struggle to order the remaining sentences when all share similar vocabulary. These failures reflect limited pattern alignment to Nomain-speciinn discourse patterns and weak implicit modeling of how scientific and policy arguments unfold. Our design, which exposes models to well-structured paper-to- brief mappings, designed to strengthen discourse-sensitie pattern alignment and implicit skills for ordering and continuations in this genre.
Understanding (Tasks 5-6). For sentence- classification (Task 5), frontier models often conflate Policy Implications with Recommendations, or mislabel methodological descriptions as findings when they contain numerical results. This indicates insufficient specialization to Sci2Pol rhetorical roles and weak style awareness of policy-brief section functions. In contrast, performance general scientific knowledge (Task 6) is comparatively strong and less affected by our domain-specific SFT, we treat Task 6 as a sanity check that specialization Sci2Pol does not destroy broad knowledge. Our corpus, built around explicit section labels and section-specito prompts, directly targets improved pattern alignment and stylistic control over these roles.
Summarization (Tasks 7-10), For policy-oriented summarization, leading models systematically underperform along two axes. First, failures in factual grounding: models often hallucinate policy framings, quantitative effects, or stakeholder groups that are not present in the selected paragraphs, especially Tasks 7 (Policy Problem) and 10 (Policy Implications). Second, failures in style and pattern alignment: generated outputs drift toward generic "science communication" prose, ignoring the requested section type (e.g., mixing methods into findings, or inserting recommendations in the policy problem). Tasks 8-9 reveal additional issues with functional- length adaptation: models either compress complex methods and findings into vague topical summaries, or over-elaborate with boilerplate explanations not grounded in the input text. Our in-context polishing prompt and SFT objective explicitly penalize these behaviors by requiring section-specific outputs, professional policy-brief style, and strict factual alignment with the target paper.
Generation (Tasks 11-15). When asked to generate sections or full briefs from the full paper, strong models exhibit compounding errors. For Tasks 11-14, they often produce sections that (i) are not cleanly separated, (ii) rely on generic policy tropes rather than paper-specific evidence, or (iii) introduce external context and speculative recommendations beyond the study's 's scope. These reflect weaknesses in factual grounding. style discipline, and implicit skill at mapping paper structure into the four-section brief template. For full-brief generation (Task 15), models sometimes maintain local coherence within paragraphs but fail at global pattern alignment: section ordering is irregular, headings are omitted or merged, and later sections contradict earlier claims. Our design addresses oection-aligned paper-brief pairs, (ii) enforcing stable four-part template in the polishing prompt, and (iii) emphasizing functional length and section-specific content during optimization, thereby improving both pattern alignment and implicit structural skills.
these issues by (i) training on high-quality
Verification (Tasks 16-18). In verification tasks, frontier models tend over-accept plausible- sounding claims, leading to high recall but poor precision. For Task 16, models often label partially supported or overstated claims as fully supported, revealing limited sensitivity to scope qualifiers and caveats in the source paper. For Tasks 17-18, they struggle to distinguish subtly contradicted policy implications from valid ones, especially when the implication is globally reasonable but locally misaligned with the provided evidence. These failure modes are rooted in weak factual grounding and insufficient disciplined reasoning about what is actually entailed by the text. While Sci2Pol SFT is not trained directly verification labels, the polishing objective, which enforces strict factual alignment and discourages speculative or extrapolatory language in generated briefs, is designed to mitigate these tendencies by cultivating more conservative, evidence-driven behaviors that transfer to verification settings.
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H.4 ANALYSIS OF IMPROVEMENTS FROM SFT
In this section, we analyze the improvements induced by supervised fine-tuning (SFT). Based on the design of the in-context polishing prompt (Table we identify four aspects of learning: factual grounding, style, pattern alignment, and implicit skill acquisition. detailed comparison of base and fine-tuned outputs is provided in Table 15 with additional analyses of leading-model shortcomings in Tables 16 [7
Factual Grounding. The in-context polishing prompt explicitly requires "strict factual alignment" with the target scientific paper and provides multiple high-quality examples. This setup strongly encourages the model improve factual grounding. Specifically, the model learns to:
Rely exclusively on the target paper. The prompt prohibits speculation and external examples, reinforeing adherence the provided scientific evidence.
Capture all major findings. SFT encourages summaries that match the scope of the study, avoiding both omission of central findings and unnecessary broadening.
Avoid hallucinations. Exposure to accurate exemplars and strict alignment requirements reduces the likelihood of invented facts or unwarranted claims.
Style. We expose models to policy-brief writing conventions, including professional tone, academic rigor, and standardized four four-part layout (Policy Problem, Scientific Research Findings, Scientific Research Study Methods, and Policy Implications). Through this structure, the model learns:
Consistent four-section structure. The SFT model generates outputs that more reliably follow the required organization.
Policy-brief writing style. Fine-tuned outputs exhibit concise, declarative phrasing, policy relevance, and precise causal language.
Improved higher-level organization. Transitions between sections become more coherent, with clearer logical connections between findings and implications.
Reduction verbosity or filler. The prompt explicitly disallows padding or oversimplification, teaching the model to maintain informational density without unnecessary elaboration.
Pattern Alignment. Table 77 we provide three (paper, brief) examples and a fixed output schema, enabling the model to learn generalizable structural patterns without memorizing specific text. The model acquired:
Abstract communicative patterns. These include framing a policy problem, narratively summa- rizing scientific evidence, and deriving policy implications strictly from the study's 'ss results. Cross-paper generalization. Although each exemplar concerns different scientific domain, their shared functional structure teaches the model to transfer these patterns to unseen papers.
Importantly, the prompt design discourages memorization of surface-level form:
The model does not reproduce specific sentences from the samples.
The vocabulary, phrasing, and order of points vary across target papers, indicating structural rather than textual memorization.
Implicit Skill Acquisition. Beyond explicit patterns, SFT teaches several latent skills that contribute to improved performance:
Funetional-length adaptation. The model adjusts the depth of discussion match the complex- ity of the target scientific paper, rather than imitating sample length.
Disciplined scientific reasoning. The SFT model more consistently avoids speculation, external examples, or causal claims not supported by the paper, and provides accurate interpretations of empirical results.
Alignment between findings and policy implications. The fine-tuned model produces implica- tions that more directly follow from the paper's findings, yielding a clearer and more logically grounded causal chain.
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Table 15: Qualitative Comparison of Gemma-27B vs. Gemma-27B-SFT on Task 15. Column 3 provides an analysis of what the model learns during supervised fine-tuning.
Gemma-27B Output
Gemma-27B-SFT Output
Analysis of Improvements
"Even when rooftop solar is free for low-income houscholds, take-up remains limited because qualified homeowners lack in- formation, face high transac- tion costs, and may distrust pro- gramme providers. Existing re- ferral schemes which efficiently connect providers with eligible clients- clients-typically rely on finan- cial rewards alone, leaving many referrals unrealised and widening clean-ent clean-energy equity gaps."
"Subsidized energy assistance programs, like those offering low- income solar installations, are vi- tal for promoting energy justice and an equitable clean energy transition. However, these pro- grams often suffer from low par- ticipation rates, leaving eligible households underserved. This under-subscription stems not only from economic barriers but also from non-economic factors like lack of information, complex pro- cesses, and distrust, resulting in inefficient allocation of resources and hindering progress towards equitable access to elean energy benefits." "
Factual Grounding + Style: SFT output maintains stronger factual alignment with the paper's framing and avoids speculative generic claims.
exhibits a more for- mal policy-brief tone ("energy jus- tice", "cquitable clean energy transi- tion"), introduces clear causal chains, and avoids compression of complex factors into short sentences. The structure is clearer and more policy- oriented.
"Adding small, unconditional US$I gift (reciprocity appeal) raised referrals by 2.6-fold com- pared with standard reward-only postcard and improved referral quality... Both interventions pro- duced durable effects, continuing to outperform the control nine months after the campaign, in- dicating that they shifted neither timing nor total referrals."
"Adding small, non-contingent gift (S1) alongside a referral re- ward reminder.. and simplifying the referral process... increased referral rates by 2.6 to 5.2 times compared to simply reminding participants the reward. This translated to 5.2 times more solar contracts at lower cost per con- tract... The quality of referrals... remained comparable to the con- trol group, and the increased vol- ume of referrals still resulted in substantial number of qualified leads." "
Factual Precision: SFT output re- ports the quantitative findings more faithfully, reflecting correct magni- tudes and their policy relevance (e.g., cost per contract, quality of referrals). Pattern Aligmment: Follows the nar- rative structure seen in the training samples- findings grouped by mech- anism, then scale, then interpretation. No Memorization: Different phras- ing and structure from samples; rea- soning aligns with the target paper.
"Rescarchers partnered with GRID Alternatives to conduct a large, pre-registered randomised controlled trial... Eleven days later, reminder postcard was sent. Referral hehaviour... was tracked for weeks and nine months. Poisson and logistic regressions... were used to estimate treatment effects."
"Researchers partnered with non non-profit administering a low- income solar program and con- ducted a randomized controlled trial... Data on referral rates, ferral quality... and resulting solar contracts were collected... and an- alyzed using statistical methods including logistic regression and Poisson count models. The study accounted for potential confound- ing factors through stratification during randomization and robust statistical analysis."
Implicit Skill Acquisition: SFT out- put expands the methods narrative with clearer explanation of data col- lection, statistical approaches, and nationale-matching the "narrative methods" requirement in the SFT prompt. Style Enforcement: Avoids bullet points, uses narrative flow, mir- rors sample briefs' level of technical detail.
"Pairing financial rewards with reciprocity and simplification multiplies programme reach at minimal incremental cost, mak- ing it practical complement to larger subsidies... Programme managers can tailor interventions their objectives: reciprocity alone yields higher-quality refer- rals, while reciprocity + simpli- fication maximises volume and cost-effectiveeess.
"Integrate Behavioral Insights... Small Incentives Can Yield Large Returns... Streamline Processes... Targeted Outreach... Dost-Effectiveness... Program Evaluation."
Style + Structure: SFT output pro- duces cleaner, bullet-pointed pol- icy implications section, matching the required format ("bullet points only"). Pattern Generalization: SFT consistently aligns implications with findings-no no speculative state- ments, each point tied to an observed effect. Improved Policy Orienta- tion: Uses the framing style of the sample briefs (action verbs, policy di- rectives).
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Table 16: Qualitative Limitations of DeepSeek- V3 on Task 15. The limitations include factual grounding. stylistic control, pattern alignment, and implicit skill acquisition.
DeepSeek- V3 Output
Analysis of Limitations
"Climate-tech startups are critical for accelerating the commercialization of innovative technologies needed global climate goals. However, these startups face high failure rates and chronic underinvestment due to long development timelines, high capital re- quirements, and misaligned investor ex- pectations."
The model introduces generic and specula- tive claims (e.g., "chronic underinvestment"" "misaligned investor expectations") that do not appear in the target paper. expands beyond the study context. weakening alignment with the evidence. Style: Tone resembles an op-ed or general commentary, not a structured policy brief: lacks concise problem framing. Pattern A Alignment: Does not follow the requested four-scction structure and drifts toward thematic discussion rather than evidence-based summary. Implicit Skills: Fails to preserve the functional depth or specificity expected from scientifically grounded brief.
Factual Grounding:
"Corporate investment strongly as- sociated with startup success (110% higher exit rate) but also with failure (62% higher failure rate), suggesting corporations selectively fund higher- risk, high-reward ventures."
The model fabricates numerical results ("I 10% higher exit rate," "62% higher failure rate"), which do not appear the original paper. Style: Uses bullet-like enumeration in Findings, deviating from the narrative style required. Pattern Align- ment: Introduces causal interpretation ("high-risk, high-reward ventures") ") not supported by the study, violating evidence-hasee reporting, Implicit Skills: Lacks disciplined reasoning: interprets correlation causally.
Factual Grounding:
"Applied Cox Proportional Hazards models assess how funding sources correlate with startup exits and fail- ures."
Substitutes the study's 's actual methods with invented ones (the real paper descriptive correlations and regression-basse comparisons, not Cox models). Style: Overly technical for policy brief norms; deviates from the narrative meth- ods requirement. Pattern Alignment: Fails mirror the structure used in the exemplars, which emphasize high-level methodological narrative rather than model-heavy detail. Implicit Skills: Does not adapt the level of technicality appropriately. violating "functional- length" guidance.
Factual Grounding:
"Target high-risk sectors underserved by private capital (e.g.. nuclear, geother- mal)... Create platforms... Mitigate risks of private investment... Leverage regional strengths."
Factual Grounding: Several points are speculative and not grounded in the paper's empirical findings (e.g.. nuclear or geother- mentioned). Style: Bullet points overly long,
mal sectors mixing policy recommendations unspecified the study. Pattern Alignment: Introduces external examples and normative proposals that diverge from evidence-based implications. Implicit Skills: Fails maintain alignment between Findings Implications; pro- posed policies traceable back the study's 's results.
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Table 17: Qualitative Limitations GPT-440 on Task 15. The limitations include factual grounding. stylistic control, pattern alignment, and implicit skill acquisition.
GPTT-4o Output
Analysis of Limitations
"California faces significant challenge in reducing greenhouse gas (GHG) emissions from its transportation sec- tor, primarily due high dependence on extraction."
The model reframes the policy problem correctly-the the paper discusses oil-extraction emissions and com- munity impacts, not transportation-Sie scctorr dependence. Style: Tone is explanatory and generic, not aligned with compact, policy-brief framing. Pattern Alignment: Does not follow the structured policy problem template (missing specific grounding in setbacks, excise taxes, and carbon taxes). Implicit Skills: Shows poor adherence to the task requirement of rephrasing the study's actual motivation.
Factual Grounding:
"Among these, sethacks yield the high- est health benefits and equity gains... but also lead more substantial worker compensation losses compared to ex- cise and carbon taxes."
Factual Grounding: Omits key quantitative details and nuances (e-g., study's nuanced 2045 target scenarios; absence of explicit percentages). Style: Narrative broad rather than specifie; lacks explicit causal reasoning tied to the paper's 's model results. Pat- tern Alignment: Loses the ordering convention used in exemplars (Findings + quantitative evidence + constraints). Implicit Skills: Simplifies complex findings, failing to preserve functional-length or depth appropriate to the study.
"The study employs a comprehensive methodology combining an empirical oil ool-production model, an air pollution dispersion model, and employment input-output model."
The original paper does not use am input- output model:; methodology is partially hallucinated. Style: Uses vague terms ("comprehensive methodology") lacking the struc- tured narrative required in SFT. Pattern Alignment: Deviates from exemplar tone, which emphasizes structured description over meta- evaluation. Implicit Skills: Does not constrain technical detail to the appropriate depth; misses mention of limitations as done in samples.
Factual Grounding:
"Policymakers must consider integrat- ing supply-side policies with demand- side strategies comprehensive coordinated reductions statewide and global GHG emissions."
Factual Grounding: Introduces policy recommendations not sup- ported by the paper (demand-side strategies are outside the study's s scope). Style: Shifts normative messaging inappropriate for scientific policy brief. Pattern Alignment: Does not adhere the exemplar requirement that policy implications he strictly derived from the Findings. Implicit Skills: Violates "strict factual align- ment" novel arguments inconsistent with paper evidence.
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H.5 IMPACT OF PROMPT LENGTH FOR TASKS 1-4
For Tasks 1-4, we test whether two sentences of context are sufficient for a model to predict the next sentence. Using Task nesting shorter prompts within the 4-sentence version for fairness. Results in Table T8 suggest that prompt length has limited effect on model accuracy.
example, evaluate 50 samples with 2-, 3-, and 4-sentence prompts,
Table 18: Impact of Prompt Length for Task1.
Task 1
Model
Len (F1)
Len (F1)
Len (F1)
Grok-3-beta
80.22:15.19
87.20:15.02
82.18:1..99
DeepSeek-R1
71.10
72.32.16.72
75.04.16.49
Qwen3-235B
40.84=6.60
44.70=1.733
50.08=8.71
GPT-4o
64.08+8.19
65.84117.62
61.9417.52
Claude-3.7-Soneet
82.60+4.95
80.18+6.01
82.30+:5.24
LLaMA-333-70B-IT
46.06:1.522
54.68
56.52.18.23
Mistral-Large
49.70=7.115
49.6848.62
49.42-t..62
DeepSeek-V3
62.88+6.98
68.3447.25
66.8447.10
AlaMA-4-Maverrae
60.1847.23
59.58
60.02.16.93
Gemma-3-27B
56.68
57.96
85.86
Gemma-3-12B
46.8816.94
50.5847.83
43.7847.82
Qwen3-8B
50.16+7.72
58.28+7.57
56.38+7.82
LLaMA-3.1-BBIIT
31.1816.41
35.9216.80
27.84
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H.6 HUMAN-LLM, JUDGE AGREEMENT EVALUATION
In this section, we evaluate whether the LLM-based judge align with human judgments. We conduct Two tests assess agreement between humans and the LLM judge.
Direct human scoring (Appendix H.6.1). We ask two human evaluators to directly score model outputs and compare their averaged scores with those produced by the Gemini judge.
(H.662 We take the top five and bottom five LLaMA-4- Task 7), have a policy expert label them as "good" or "bad", and compute confusion matrix to assess judge reliability.
Confusion-matrix validation Appendix [H.6.2]
Maverick summaries (ranked by Gemini-2.5-Pro
H.6.1 DIRECT HUMAN SCORING
We randomly sample 10 cases from Tasks 9 and 11-15 for human evaluation (using the same 10 cases across Tasks 11-15). Due to the limited number of evaluation questions, we report only the final results and do not conduct bootstrap significance testing. The results show that Gemma-27B- SFT consistently outperforms DeepSeek- V3 and GPT-4o, aligning closely with our reference-based evaluation. For completeness, we present one representative example for Tasks 9 and 15 along with
brief analyses in Tables 20
Table 19: Direct Human Scoring.
Task 9
Task
Task
Model
Gemini
Human
Gemini
Human
Gemini
Human
Gemma-3-27B-SFT
83,40 1.03
82.00
91.7611.90
89,20
78.26 1.35
75.10
DeepSeek-V3
76.90
74.00
91.5010.93
88.40
78.24=1.46
75.60
GPTT-0o
71.3810.06
70.00
89.17:1.998
86.30
7.069=1.05
71.30
Task 13
Task 14
Task 15
Model
Gemini
Human
Gemini
Human
Gemini
Human
Gemma-3-27B-SB7
67.34
65.20
86.65 1.53
83.25
83.65
80.00
DeepSeck-V3
90.29-10.99
84.50
87.62
84.50
83.51++1.65
79.10
GPT-4o
72.81
68.20
78.36 1.54
78.25
68.93-41.31
65.90
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Table 20: Examples of Task 9 Outputs Across Different Models.
Question:
You are given passage from a scientific paper that describes part of the research study's 's methodology. Summarize the method or approach using policy-briet style sentences. Your output should:
Describe the model, data, or procedure mentioned in the passage
Use clear and accessible language (technical terms are allowed when necessary) Focus only on what is present in the passage.
Scientific Text:
Our research methodology is informed by the principles underpinning ethical Australian Indigenous research outlined the Australian Institute of Aboriginal and Torres Strait Islander Studies Code of Ethics for Aboriginal and Torres Strait Islander Research. Our research team is committed to the principles of Indigenous sefffdctermination, Indigenous leadership, impact and value, sustainability and accountability. V.N.D. is senior Aboriginal rescarcher Tangentyere Research Hub in Mparttwe (Alice Springs) and a visiting Indigenous fellow the Australian National University (ANU) Centre for Aboriginal Economic Policy Research. M.K. is senior policy manager at Tangentyere Research Hub and a visiting fellow at ANU's ' Centre for Aboriginal Economic Policy Research.
Summary:
Answers (Study Methods Summarization)
(i) GemSa-27B-SFT
(ii) DeepSeek-V2
(iii) GPT-40
To identify plausible control areas that could have been designated as part of the Coastal Barrier Resources System (CBRS) in 1982 but were not, researchers used an automated pro- cess mirroring the original manual Moundary-drawin approach. They relied on historical data- intlu-inn aerial photos, topographic maps, and 300-meter- resouuuutnonttionn grids of land cover, development, clevation, and coastal proximity- to replicate the information available to planners the time. Only coastal grid cells within 2 km of the shore were consid- ered, while excluding areas that were fully water, already part of the CBRS or other protected areas, or within 2 km of existing CBRS units. This method ensured control arcas were comparable to actual CBRS units terms of geographic and developmen- characteristics.
The study identifies potential control areas for Coastal Barrier Resources System (CBRS) designation from 1982 by mimicking the original selec- tion criteria through an automated ap- proach. This method involves analyz- ing aerial photographs, topographic maps. and 300-meter resolution grid- ded data on land cover, development, elevation, and proximity the coast. Only areas within 2 kilometers of the coastline are considered, excluding entirely water-covered cells, those within existing or planned CBRS units, protected areas, and areas near CBRS units to prevent overlup of ef- fects.
Potential control areas were digitally delineated from 300 m raster data on land cover, development, eleva- tion and coastal proximity, excluding cells within or near existing or pro- tected coastal zones, mimic plan- ners' 1982 information and drawing process.
Analysis:
Gemma-27B-SFF provides the strongest policy-brief style summary. Its response the most concise and the most aligned with policy-focused communication norms, distilling the method into a single clear statement without unnecessary operational details. Compared with DeepSeck- V3 and GPT-4o, which produce much longer and more technical deseriptions, Gemma-27B-SHP captures the essential methodological mechanism with precision and clarity. This makes Gemma-278 27--a--7B-SST's output more accessible, more readable, and better suited for policy bricf's that prioritize streamlined, high-level methodological reporting.
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Table 21: Examples of Task 15 Outputs Across Different Models Part 1 (Problem & Findings), Scientific Paper: "What is driving the drug overdose epidemic in the United States?" TTombs et al. 2020b)
(i) Deemma-27BSSB
(ii) DeepSeek-V3
(iii) GPT--0
What is driving the drug overdose epidemic in the United States?
What is driving the drug overdose epidemic in the United States? Policy Problem Despite two decades of escalating intervention, the United States continues to face a lethal drug- overdose epidemic that claimed more than 70,000 lives 2017 alone- an age-adjusted rate of 21.7 deaths per 100,000, more than triple the 1999 level. New evidence shows that the crisis no longer driven solely by prescription-opiodd misuse by "deaths of despair" among less-educated workers; instead, it re- flects dangerous convergence of (1) abundant, increasingly synthetic opioid supply (licit and illicit) and (2) widening income inequality that strips the nottom-quintile of earners of economic and social buffers. State- level analysis (2006-2017) reveals that while higher rates predict higher overdose mor- tality, educational attainment does not, and the strongest incquality pre- dictor is low income share for the bottom 20 percent- gains at the top. Current policy, which centers on regulating prescribers and expanding treatment, therefore tackles only half the problem; without addressing the structural deprivation that fuels de- mand for high-risk substances, over- dosc deaths and and their spill-over to stimulants like cocaine and metham- phetamine will likely continue to
What is driving the drug overdose epidemic in the United States? Policy Problem The United States is facing a devastating drug overdose epidemic, which remains a signifi- cant public health crisis. In 2017, the country witnessed 70,237 drug overdose deaths, with opioids being significant contributor, accounting for 67.8% these fatalities. While focus has often been on opioids, cocaine and psychostimulants are creasingly involved, highlighting the evolving nature of this epidemic char- acterized by polysubstance use. This crisis reflects broader socioeconomic disparities and necessitates under- standing of its complex drivers in- form effective public health policies. Scientific Research Findings 1. Ed- ucational attainment docs not signifi- cantly predict overdose mortality. 2. Opioid prescription rates are posi- tively associated with drug mortality. 3. Income incquality is a key deter- minant: lower income share for the bottom 20% correlates with higher overdose mortality.
Policy Problem Despite decades of U.S. policy intervention, drug- overdose deaths continue to rise, ex- ceeding 70,000 in 2017. The cri- sis now reflects the interaction two structural forces: (I) increas- ingly potent and widely available opioids, both licit and illicit, and (2) deepening economic inequality that crodes stability for the poor- households. State-level analyses from 2006-2011 2017 show that opioid availability predicts overdose mortal- ity, whereas educational attainment does not. The most powerful inequal- ity predictor is the declining income share of the bottom 20%. Without ad- dressing both supply-side drug avail- ability and structural cconomic depri- vation, overdose mortality will likely continue climbing and spreading into other substances, including cocaine and psychostimulants.
Scientific Research Findings The income share held by the bottom 20% is the strongest inequality pre- dictor of drug-related mortality. In- creasing the bottom-quine suittill income share corresponds to reduced over- dose mortality. States with higher opioid prescription rates have higher drug-related mortality on average.
Scientific Research Findings Of the four measures of income inequal- ity that we used, the share of income going to the bottom 20% was the key inequality measure associated with drug-related mortality. An increase in the share of income going to the bottom 20% of earners was associ- ated with reduction in drug-related mortality. States with higher opi- oid prescription rate, on average. had higher drugerelated mortality.
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Table 22:
Examples of Task 15 Outputs Across Different Models - Part 2 (Methods, Implications, Analysis). Scientific Paper: "What is driving the drug overdose epidemic in the United States?" (Thombs et al. 20206)
Gemma-27B-SFT
(ii) DeepSeek-V3
(iii) GPT-40
Scientifie Research Study Methods The study links CDC WONDER tality data, CDC opioid-prescribon data, and ACS inequality and ed- ucation statistics for all 50 states and DC (2006-2017; 611 observa- tions). All continuous variables were logged and appropriately centered. A two-level within-betweed random- intercept model disentangles tempo- ral variation within states from struc- tural differences across states. Co- variates include median household income, manufacturing employment share, census-region indicatons, and quadratic time trends.
Scientific Research Study Methods We compiled balanced panel of all 50 states plus DC for 2006-2017 merging
Scientific Research Study Methods The rescarch
two-level dom intercept model with state-level data from 2017, covering all 50 states and DC. This method al- lows for the simultancous unalysis of within-state and between-state vari- ation in education, opioid prescrip- tions, income inequality. and drug mortality.
(N 611 state-years) CDC WONDER drug-overdose mortality rates with (i) CDC opioid-prescribing rates, (ii) ACS educational anttaateonllatttaimmnntt and income- distribution data (shares of the top 5%, top 20%, bottom 20%, and the Gini), and (iii) state economic structure and region controls. All continuous variables were logged and centred. Two-level random- intereept models separated within- and between-state variation.
Policy Implications Policies should tighten regulation of opioid preserib- ing and marketing, strengthen moni- toring systems, and address structural economic incquality. Redistributive policies-sure as progressive taxa- tion, higher minimum wages, and expanded social services-come im- prove cconomic stability among vul- nerable groups and reduce overdose risk.
Policy Implications Effective pre- vention requires targeting both drug supply and structural incquality. Pol- icymakers should regulate opioid manufacturing, distribution. and pre- scribing practices. while implement- ing redistributive reforms that raise economic position the lowest- income houscholds. Strengthened safety nets, progressive taxation, wage supports, und hcalthcare reform can mitigate structural vulnerability and reduce the population-leve risk of overdose mortality.
Policy Implications Our findings suggest that an adequate prevention response will require both limiting the availability opiates and redis- tributing income, especially to earn- ers at the bottom of the income distribution. Policy makers should strengthen regulations on opioid man- ufacturing, distribution, and prescrip- while addressing structural economic inequality to reduce vulner- ability in low-resource communitics.
Analysis: Cemmma-27B-Si provides the strongest overall policy brief. is the most concise. maintains a clean policy-bricf structure, and links empirical findings directly to actionable policy recommendations. DeepSeck-VS is detailed but overly long and less suited to policymaking audiences; GPT-4o is clear but less structurally rigorous, Gemma therefore provides the most interpretable and policy- relevatt response.
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H.6.2 CONFUSION-MATRIX VALIDATION
In this part, we evaluate Gemini's ' scoring of LLaMA---Maverid outputs on Task as a toy example. Specifically, select the five samples that receive the highest scores and the five that receive the lowest scores from Gemini-2.5-Pro, We then ask our political expert to review these ten samples and determine which are "good" or "bad" summarizations (5 "good" and 5 "bad" summarizations) We show the confusion matrix in Table 23 and the results show that our reference-free method is indeed reliable.
Table 23: Confusion Matrix of the Gemini-2.5-Pro-bass Reference-free Judge. We evaluate Gemini's ' scoring of LLaMA-4-Maverick outputs on Task 7 as toy example. Specifically, we select the five samples that receive the highest scores and the five that receive the lowest scores from Gemini-2.5-Pro. We then ask political expert review these ten samples determine which are "good" or "bad" summarizations "good" and 5 "bad" summarizations). The confusion matrix shows the reliability of this method.
Expert Judgment
Predicted Good
Predicted Bad
Good
Bad
5
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H.7 WHY TASKS 11-14 BEYOND TASK 15: SECTION VS. FULL BRIEF GENERATION
We provide a concrete example comparing LLM output generated section-by-section versus as full policy brief. Specifically,
L.L.MA-3.-8801 -nStttrccttt on the paper European battery self-
sufficiency (Link al. 2025a). We show the results in Table and Table 25 This example illustrates why Tasks 11-14 necessary in addition Task 15 Sci2Pol-Bench.
Task 15 evaluates end-to-end policy brief generation, but Tasks 11-14 aim to capture comple- mentary dimensions of model ability. When LLMs generate briefs one section at time, they often include more technical details: precise figures, dataset sizes, or methodological constructs. This section-wise mode highlights a model's capacity to reproduce fine-grained scientific evi- dence. However, the sections may drift in terminology, exaggerate findings, or fail to connect implications back to results. The outputs are accurate in isolation but lack global coherence.
In contrast, full-brief generation produces more fluent and coherent outputs. The narrative flows smoothly from the policy problem to the findings, methods, and implications, often with organized recommendations and consistent tone. Yet this approach tends to sacrifice technical precision. Numbers are simplified, mechanisms skipped, and caveats dropped. These lead to overconfident generic claims that weaken policy relevance.
Thus, Tasks 11-14 complement Task 15 by disentangling precision from coherence. Section-level generation stresses factual grounding and attention to detail, while full-brief generation stresses holistic structure and readability. Evaluating both provides a fuller picture of LLM performance and reveals trade-offs that Task 15 alone cannot capture. These comparisons justify the inclusion of Tasks 1-14 as critical complements to Task 15.
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Table 24: Section-by-sectio vs. Full-brief Generation. We use European battery self-suffccieccy (Link et al., 2025a). The full brief (Task 15) shows smoother narrative and coherence, while the section-by-section outputs (Tasks 11 11-14) surface fine-grained technical precision.
on the paper
Task 15: Full brief
Tasks 11-14: Sections
The European Union's 'ss ambi- tion to strengthen national capabilities and self- sufficiency in battery production is risk due uncertain demand and production growth rates. The study highlights the need for stakeholders to accelerate production capacities and reckon with demand growth post-2030 to support Europe's battery prospects.
Policy Problem:
Task 11 (Policy Problem): The European Union's ambition to strengthen national capabili- ties and self-sufficiency in battery production is at odds with the growing demand for batteries in the region. By 2030, European battery demand is projected exceed 1.0 TWh/yr, far outpacing domestic production capacity, which will require highly ambitious growth rates of 31 31-68% Our research suggests that while Europe can cover at least 50-60% % of its demand via domestic produc- tion by 2030, 90 % self-sufficiency seems feasible but far from certain. This exposes Europe to the risk of domestic production shortfalls, decreased competitiveness, and reliance on imports, threat- ening to undermine the region's 's net-zero goals and climate change mitigation efforts. Immedi- atc attention is needed from policymakers and industry leaders to overcome these production challenges, strengthen supply chains, and ensure sustainable battery development.
(Sets stakes succinctly and frames the policy are; coherence-first franing without rying modelling choices yet.)
(Numbers and risks front- loaded; standalone problem statement precise but disconnected from other sections.)
Scientific Research Findings: The study uses probabilistic modeling to project future battery demand and domestie production in Europe, cov- ering the EU, EFTA, and the United Kingdom. The results indicate that:
Task (Findings): The study finds that Eu- rope is likely to experience ambitious growth in battery demand, reaching at least 1.0 TWh by 2030, with a high probability of surpassing 1.0 TWh by 2026. However, domestic production capacities are more likely to fall behind terawatt hour scales by 2030, with momentum increasing after 2025. Despite this, it is very likely that Eu- rope can cover at least 50-60 % of its demand via domestic production by 2030, and even 90 % self-sufficiency seems feasible but from cer- tain. The study emphasizes the urgent need for reliable policies to ensure domestic production and develop a comprehensive battery ecosystem, including localization of raw material production and recycling. To achieve this, stakcholders must accelerate the materialization of production ca- pacities and consider the potential for ambitious growth rates production capacity.
European demand is likely exceed 1.0 TWh/yr by 2030, outpacing domestic pro- duction.
Domestic production capacity is more likely to fall behind terawall hour scales by 2030, with momentum increasing after 2025.
At least 50-60 % of demand can be covered by domestic production by 2030.
90% self-sufficiency by 2030 seems feasible but far from certain.
(Aggregates key quantities clearly for readability; sce- nario gramuaarty and caveats are compressed com- pared section-level detail.)
(High technical precision in probabilities, thresholds. and timelines; but no linking back to policy problem or methods.)
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Table 25: Section-by-section vs. Full-brief Generation (Continued from Table 24
Task 15: Full brief (continued)
Tasks 11-14: Sections (continued)
Scientific Research Study Methods: The study employed probabilistic modeling approach, us- ing the latest empirical data to project future bat- tery demand and domestic production in Europe. The model was implemented in Python, and the results were based on a Monte Carlo simulation (N=1,000) to construct feasibility spaces and clas- sify findings by probability.
This study projects future battery demand and domestic production capacity Europe using probabilistic modelling. The research design combines empirical data with a Monte Carlo simulation (N=1,000) cre- ate feasibility spaces and probabilistic statements. The model evaluates Europe's prospects towards battery self-sufficiency and was implemented in Python. The study focuses on high-energy bat- teries for battery electric vehicles (BEVs), which are the main demand driver. European demand is projected increase, but domestic production capacity likely of domestic production shortfalls. The study also analyzes the European battery value chain, in- cluding raw material sourcing and recycling, and highlights the importance of reliable industrial policies to support Europe's 's competitiveness in the battery industry.
Task 13 (Methods);
(Method summarized compactly; omits modelling ar- tifacts and domain specifics that appear explicitly Task 13.)
behind, indicating risk
(Technical scaffolding explicit here: modelling op- proach, simulation size, demand drivers. This detail is absent in Task 15's smoother summary.)
Policy Implications:
ensure a competitive and sustainable battery ecosystem in Europe, polieymakers and industry stakeholders must take immediate action to support domes- tie production expansion and address potential risks. Our study highlights the need for indus- trial policies that balance trade protectionism and global competitiveness, creating attractive and predictable home markets that reduce risks for industry players. Reliable policies crucial lower risks and ensure certain predictability. To achieve 90% self-sufficiency by 2030, stake- holders must accelerate the materialization of pro- duction capacities and consider demand growth post-2030. 2030. Investments in local battery produc- lion are necessary to avoid shortages domestic production while keeping track of international developments.
Task 14 (Policy Implications):
Accelerate production capacities to meet growing demand and support Europe's bat- tery prospects.
Reckon with demand growth post-2030 to en- sure a stable and sustainable battery ecosys- tem.
Implement industrial policies that balance trade protectionism and global competitive- ness to create attractive, predictable home markets.
Invest in local battery production to avoid shortages in domestic production and keep track of international developments.
Prioritize establishing fully scaled and sustain- able value chains.
Use net materialized production capaci- ties as basis for projections rather than an- nounced capacities.
(Concrete recommendations are explicit but discon- nected from modelling details nunerical findings. Lacks coherence across sections.)
(Actionable storyline; recommendations not explicitly re-anchored to each numerical threshold method assumption.)
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H.8 EVER-ENDORSEMENE ANALYSIS ON TASKS 16 AND 18
We take Tasks 16 and 18 as examples to conduct an over-endorsement analysis by comparing predicted labels ("SUPPORT" "CONTRADICT") against ground-truth annotations. Specifically, we compute the number of false positives-instanee where the model predicts "SUPPORT" despite the true label being "CONTRADICT" well as false negatives, where the model fails endorse a true "SUPPORT" case by predicting "CONTRADICT". For each task, we calculate the false positive rate (FPR), defined as the proportion of CONTRADICT-labele predicted as SUPPORT, and the false negative rate (FNR), defined as the proportion of SUPPORT- labeled instances that are incorrectly predicted as CONTRADICT. We also compute the ratio of false positives to false negatives (FPR/ENR ratio). A high FPR/ENR ratio indicates model's 's propensity to over-endorse, potentially overstating the strength of scientific support behind science and policy claims. This analysis provides insight into models' decision biases, which is critical for assessing their reliability in science and policy-relevant contexts.
instances that are incorrectly
We show the detailed results in Table 26 and we have the following findings:
The FPR/ENR ratios show consistent results across Tasks 16 and 18, with the exception of the model DeepSeek- V3, which exhibits divergent behavior.
Most advanced models not exhibit over-endorsement, whereas smaller models such as LLaMA- 3.3-70B-IV IT, Gemma-3-278 Gemma-3- 3-B,, and LLaMA-3.1-8B-I demonstrate tendency to over-endorse scientific claims and policy implications.
Table 26: Over-endorsement Analysis on Tasks 16 and 18. We compute the number of false posi- tives tives-instances where the model predicts "SUPPORT" despite the true label being "CONTRA- DICT"-as well as false negatives, where the model fails to endorse a true "SUPPORT" case by predicting "CONTRADICT". For each task, the false positive rate (FPR) is defined as the propor- tion of negative rate (FNR) is defined the proportion of SUPPORT-labeled instances that are incorrectly predicted as CONTRADICT. We also show the ratio of false positives to false negatives (FPR/ENR ratio). high FPR/ENR ratio indicates a model's propensity over-endorse, potentially overstating the strength of scientific support behind science and policy claims.
instances that are incorrectly predicted as SUPPORT, and the false
Task 16
Task 18
Model
FPR
FNR
FPR
FNR
FPR
FNR
FPR / FNR
Grok-3-beta
0.47
2.34
0.2000
0.57
2.56
0.2222
DeepSeek-RI
0.95
10.05
0,0930
1.72
3.99
0.4286
Qwen3-235B
0.00
8.88
0.0000
0.86
4.56
0.1875
GPT-40
0.24
2.57
0.0909
0.29
4.84
0.0588
Claude-3.7-Somnet
0.24
3.04
0.0769
0.29
2.85
0.1000
L.A.MA-3.--0BBIIT
0.95
0.93
1,0000
4.01
2.28
1.7500
Mistral-Large
0.95
7.94
0.1176
2.58
16.24
0,1579
DeepSeek-Ve V3
0.95
1.17
0.8000
2.29
1.71
1.3333
I.LaMA-4-aaverick
0.71
5.37
0.1304
1.72
3.42
0,5000
Gemma-3-27B
2.13
1.4
1.5000
8.31
2.56
3,2222
Gemma-3-12B
5.92
2.34
2.5000
10.60
0.85
12.3333
Qwen3-8B
0.24
14.49
0.0161
1.43
7.12
0.2000
LLaMA-3.1-8B-IT
18.01
1.87
9,5000
18.34
2.85
6.4000
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H.9 INFORMATION LEAKAGE CHECK OF IN-CONTEXT POLISHING IN SECTION 3.3
When applying in-context polishing to revise policy briefs (Section 3.3 we use three reference samples from the 85 expert-written briefs. The three reference samples
Reliable industrial policies required to support the ramp-up of European battery production
Paper (Link al. 2025a); Brief (Link al. 2025b).
How central banks address climate and transition risks Paper (Shears et al. 2025a); Brief (Shears et al.
2025b).
Faster deployment of renewables stabilizes electricity prices in Europe Paper Navia Simon and
Diaz Anadon 2025b); Brief (Navia Simon and Diaz Anadon 2025a).
Although the prompt for GPT-o3 instructs the model to mimic only writing style and format (Table 77 there remains risk of information leakage. To validate that this step does not introduce leakage, we perform three ablation studies:
Excluding the three samples from Task 15 and then using Task 15 for justification (Ap- pendix 1.9.1).
Using the three latest published paper- brief pairs (detailed Appendix J.4) as the in-context examples to polish the dataset, and subsequently using this newly polished dataset for supervised tuning (Appendix [..9.2).
Using an additional 8 newly published samples (after May 10th, 2025, detailed in Appendix J.3 as the new samples for Task 15, and testing the model performance on these samples with Task 15 (Appendix H.9.3). H....)..
H.9.1 EXCLUDING THREE SAMPLES FROM JUSTIFICATION
We exclude the three samples from Task and use Task 15 for justification. We show the results in Gains remain stable, and this shows that in-context polishing does not induce leakage.
Table 27
Table 27:
Information Leakage Check In-context Polishing Section 3.3 Performance on Task 15 with all 85 pairs vs. with the three reference samples removed (82 pairs). Gains remain stable, and this shows that in-context polishing does not induce leakage.
85 Expert-written Pairs
82 Expert-written Pairs
Model
T15 (Reference-based Score)
Gain
T15 (Reference-based Score)
Gain
LLaMA-3.1-8BIIT
59.05=1.55
58.77=1.62
LLaMA-3.1-BB-SFT
7..76=1.75
+13.71
72.32=1.90
+13.55
Gemma-3-128
+5.71+1443
75.49+ 1.47
Gemma-3-122-SFP
76.06
+0.35
1.77
+0.61
Gemma-3-277
80.89=1.26
80.30=1.30
Gemma-3327B-SFT
83.65=1.75
+2.76
13.54_11.76
+3.24
H.9.2 IN-CONTEXT POLISHING WITH THREE NEW PUBLISHED PAIRS
We use the three latest published prier-brief pairs as the three in-context examples to polish the dataset, and use this newly polished dataset do supervised tuning. The three examples include:
Nutritional outcomes of irrigation expansion Paper Mehtt et al. 2025a): Brief (Mehta et al. 2025b).
Managing development choices is essential to reduce coastal flood risk in China et al. a (025c); Brief (Wang et al. 2025b).
Paper
Wang
Heat pumps can help alleviate residential energy insecurity in the USA Paper Ye et al. Brief (Ye et al.] 2025b).
2025a
We show the results in Table 28 Gains remain stable, and this shows that in-context polishing does not induce leakage.
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Table 28: Information Leakage Check of In-context Polishing Section 3.3 using three new non-overlapping in-context polishing examples.
Performance on Task 15
Original In-context Samples
New 3 In-context Samples
Model
T15 (Reference-based Score)
Gain
T15 (Reference-based Score)
Gain
L.L.MA33.--B--T
+..05+1.555
59.05 +1.55
LLaMA-3J1SB-SPT
7..76=11.75
+13.71
72.91
+13.86
Gemma-3-12B
75.71
75.71
Demma-3-122-SBB
76.06+ 1.66
+0.35
76.31+1.66
+0.60
Gemma-3-277
80.89 1.26
80.89
Gemma-3-27B-SFT
$3.65.11.75
+2.76
83.43.1 1.80
+2.54
H.9.3 EVALUATING ON NEW EIGHT TEST SAMPLES
We use another eight newly published paper-brief pairs generate Task 15. These include all the new peer-reviewed pairs to Nov. 12th, 2025, We list the details of these eight new pairs in Appendix [.3 We show the results in Table 29 Gains remain stable, and this shows that in-context polishing does not induce leakage.
Table 29: Information Leakage Check 3 of In-context Polishing in Section with eight new samples.
3.3
Performance on Task 15
Original 85 Expert-written Pairs
New 8 writee- Pairs
Model
T15 (Reference-based Score)
Gain
T15 (Reference-based Score)
Gain
-LaMA-3.1-8B-IT
19.05=11555
58.43::1.12
LLMSAF3.1-8B-FFT
1.75
+13.71
72.02.11.36
+13.59
Gemma- 3-12B
75.71+1.443
74.81+1.12
Gemmm-3--12BSSB
76.66=11.66
+0.35
1..5
+0.77
Gemma-3-27B
1.26
81.02:11.62
Gemma- Gemma-3-27B-SFT 3-27B-SFT
1.765
+2.76
83.21 1.44
+2.19
DeepSeck-Ve V3 GPT-4o
83.51
82,52
68.93-11.31
67.53-11.59
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H. ATHLETE AS JUDGE TESTING: GPT FAMILY
common concern in benchmark design is potential circularity when GPT family models e.g.. GPT-o3) generate intermediate data, such classification labels gold prompts for tasks where other GPT family models (e.g., GPT-4o) are later evaluated. This "athlete as judge" setup could favor models from the same family because of shared training signals and writing style. To test this, design two experiments:
Cross-lineage evaluation: Evaluate the performance of GPT-4o and DeepSeek-Ve V3 datasets generated by GPT-o3 or a different lineage model, DeepSeek-RI (Appendix H..0O.).
Model substitution: Replace GPT-o3 with MiniMax-M2 the data construction process (Ap- pendix H..0.
H.10.1 POTENTIAL CIRCULARITY: GPT VS. DEEPSEEK FAMILY
To test whether this issue affects Sci2Pol-Bench, we conduct a controlled comparison on Task 16. We evaluate GPT-40 and DeepSeek- V3 on datasets generated either by GPT-o3 or by different lineage model, DeepSeek-RI. As shown in Table 30 DeepSeek-V3 consistently outperforms GPT-4o, and both models drop 10-12 points when prompts come from DeepSeek-R. instead GPT-o3. DeepSeek-RI prompts harder. This demonstrates that any benefit from GPT-o3 prompts applies broadly across models rather than giving GPT-family models a special advantage, and it confirms that the "athlete as judge" concern does not affect setting.
Table 30: GPT DeepSeck Family Task 16. DeepSeek- V3 consistently outperforms GPT-4o, and both models drop 10-12 points when prompts come from DeepSeek-RI instead of GPT-o3. DeepSeck-RI prompts harder. This demonstrates that any benefit from GPT-o3 prompts applies broadly across models rather than giving GPT family models a special advantage, and it confirms that the "athlete as judge" concern does not affect our setting.
Model
Dataset (GPT-o33
Dataset (DeepSeek-RI)
GPT-40
98.61 0.39
86.93 1.26 89.00 = 1.06
DeepSeek-V3 V3
98.97 = 0.37
H.10.2 MODEL SUBSTITUTION BY MINIMAX-M2
GPT-o3 is utilized generate data for Tasks 5, 11, 13, 16, and 18, and to polish all corpus documents. To assess the influence of this model choice, we design an ablation study using Tasks 11 and 13 as illustrative examples. The experiment proceeds as follows: (i) We replicate the original data construction method, but replace the data generation model (GPT-o3) with MiniMax-M2.
(ii) We then evaluate the performance of GPT-4o, DeepSeek- V3, and Mistral-Lan Large on the data constructed by MiniMax-M2 for Tasks 11 and 13. (iii) The evaluation utilizes dual LLM Judge setup, employing both Gemini and MiniMax-MP The full results are presented in Table 3T These results demonstrate consistency when employing different large language models in the dataset construction process.
Table 31: Model Performance Tasks 11 and 13 with Dataset Constructed MiniMax-M2.
Gemini Judge
MiniMax-M2 Judge
Data Construction
GPT-o3
MiniMax-M2
GPT-o3
MiniMax-M2
Model
T11
T13
DeepSeck-V3
91.50-00.93
90.29-00.99
91.
89.93--0.94
54.32+2.44
65.56
54.98-+2.34
65.78=1.1 II
GPT.-40
12.81
89.53-1094
52.01:12.40
6.283
Mistral-Large
87.64++0.22
10+1031999
87.02++0.97
63.98=1.344
11.71+1.56
61.64+1.46
50.94+1.38
61.03=1.44
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H.11 SATURATION TESTING
We do the saturation analysis as in Figure We report the relationship between Sci2Pol-Bench performance and three commonly used model characteristics: (a) model size, (b) pre-training FLOPs, and (c) release date. Although several model developers do not publicly disclose all training details, the approximations used here follow standard practice in the LLM evaluation literature and provide reliable, order-of-magniudee comparisons.
(a) Model Size
(b) Pre-training FL.PPs
(c) Release Date
Figure 4: Satuation Testing: Model Size, Pre-training FLOPs, and Release Date.
Score vs. Model Size. Very large models (e.g., GPT-4o) perform strongly, but several mid-sized models such as DeepSeek- V3 match or exceed them, indicating that parameter count alone does not determine policy-reasoning quality. Smaller models (<. 10B) consistently underperform, suggesting minimum capacity threshold. For proprietary models that do not release full architectural details, we use the closest publicly stated or family-level parameter estimate. These values are widely used by open-source leaderboards and preserve the correct relative placement among models.
Score vs. Pre-training FLOPs. Although the highest-FLOPs models generally achieve the strongest results, many models in the 1024-10'3 FLOP range overlap substantially, showing diminishing returns once compute passes a sufficient scale. Several developers no longer publish exact FLOP counts. Therefore, follow the standard estimation heuristic FI.OPs As 6 N Nprrams Tinkens using known or reported model sizes, token counts, and architectural multipliers. Even when exact numbers unavailable, the order-of-magnitude estimates robust and do not affect the qualitative scaling trends.
Score vs. Release Date. The third panel visualizes performance relative the earliest publicly documented release month. While newer models (2024-2025) generally occupy the upper perfor- mance range, the significant variation observed within yearly cohorts indicates that recency is not the dominant factor. Instead, our supervised fine-tuning results indicate that aligmment strategies and the specific data mixture used in training show a stronger influence on Sci2Pol- Bench performance.
Overall, the three panels show that Sci2Pol-Bend Bench performance does not follow simple scaling law. Model size, compute, and release date each matter, but do not fully explain pross- differences. Instead, high performance is often associated with improved alignment strategies, targeted supervised finetuning, and richer training mixtures. These are factors that help models reason about scientific evidence and policy trade-offs beyond what raw scale alone can provide.
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H. COMPARISON OF WRITING FROM AN ABSTRACT, AN INTRODUCTION, AND FULL PAPER
We compare policy brief generation when the input is the abstract, the introduction, the full scientific paper. Writing from the full paper requires a much longer input context. This increases inference cost, fine-tuning cost, and technical difficulty. At the same time, offers richer information for grounding policy-relevant insights.
We first present concrete example using Grok-3-beta generate policy briefs for the paper How central banks address climate and Iransition risks (Shears et al. 2025a), based on three input scopes: (i) the full paper, (ii) the introduction only, and (iii) the abstract only. We show the generated outputs in Table 32 and Table 33 The outputs enable a detailed comparison across input settings as follows.
When model writes from the abstract only, it captures the headline claim, the main outcome, and the high-level implications. The tone reads confident, and the structure feels clean. However, the draft lacks mechanism, methods detail, and scope limits. Numbers, when present, drift toward round values, and uncertainty language fades. Policy advice turns generic, because the draft does not anchor actions to specific evidence or design choices. The abstract frames motivation and result, but it does not carry the caveats that make brief credible, the model tends to overstate precision and under-specify risks.
When model writes from the introduction only, it provides better context. The draft states the policy stakes, defines terms, and maps the debate. The flow improves, and the problem framing reads stronger. Yet, the introduction centers motivation and gaps, not verification. Methods remain thin, and heterogeneity across cases stays offstage. Claims may adopt the paper's s persuasive setup, which can tilt the brief toward advocacy language. Without the full evidence base, the draft still softens uncertainty, skips mechanism, and treats recommendation. at the slogan level, not at the instrument level.
When model writes from the full paper, it can link evidence to tools. The draft cites methods, describes mechanisms, and carries uncertainty statements. It notes limits, and distinguishes what is feasible from what is likely. The narrative supports actionable advice, because the draft can name concrete instruments, timing, and trade-offs. Here, the main risks shift. The model can drown readers in detail, mix minor numbers into the main thread, or copy framing that belongs a technical appendix. If the paper is dense, the model may misread tables or blend results, so careful summarization still matters.
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Table 32: Task 16 Outputs Across Input Scopes. We use Grok-3-beta to generate the policy brief for How central banks address climate and transition risks (Shears et al., 2025a) from three input scopes: (i) full paper, (it) introduction only, and (iii) abstract only. This comparison supports our analysis in Appendix H.12
The table illustrates how source scope affects Task 15 output quality.
(i) Full Paper
(ii) Introduction Only
(iii) Abstract Only
Policy Problem
Climate change and the global transi- tion away from fossil fuels present sub- stantial risks to economic and finan- cial stability. These include "transition risks" (e.g (e.g. losses on fossil fuel assets becoming obsolete, known as stranded asscts) and "physical risks" (e-g.. coo- nomic damage from storms, droughts). Central banks, responsible for finan- cial stability, have begun addressing these climate-related risks. However, their actions vary significantly across countries, leading uncertainty about whether these crucial institutions effectively managing the underlying economic dangers are primarily in- fluenced by other factors. This incon- sistency creates a potential gup in mit- igating systemic financial risks related to climate change.
Climate change and the global transi- tion to clean energy create significant risks for the financial system, includ- ing "stranded assets" (devalued fossil fuel investments) and the uncertain- ties of new clean energy technologies. These risks threaten the stability of national and global economies. Cen- tral banks have been identified as key institutions
Electricity price volatility, driven by unpredictable fluctuations in global fossil fuel markets, poses a signif- icant economic risk to households, businesses, and national economies. As Europe transitions its energy sys- tem, critical question for policy- makers is whether high share termittent renewahles, like solar and wind, will increase or decrease this price instability. Understanding this relationship is essential for designing resilient and cost-eflective long-term energy strategies.
managing these finan- cial risks, yet their response hus been highly inconsistent across countries. This uneven approach creates criti- cal policy challenge: understanding what drives central banks to act on climate risk is essential for ensuring financial stability and supporting an orderly energy transition.
Scientific Research Findings
The study reveals considerable differ- ences in how central banks in major cconomics (OECD and G20) are man- aging climate risks. Researchers cat- egorized actions into "re-risking" (in- tegrating climate risks into financial supervision, targeting fossil fuels and physical impacts) and "de-risking" (re- ducing investment risks for clean en- engy). Key findings indicate:
The study reveals that central bank actions on elimate risk are not pri- marily driven by the magnitude of the underlying economic threats. In- stead, political factors the most significant driver.
The research demonstrates that newable energy sources have pow- erful stabilizing effect on electricity prices. Key findings include: By 2030, under current national en- ergy plans, the Furopean power sys- tem will be significantly less vulnera- ble to natural gas price shocks. A El increase in the price of natural gas is projected to raise the average eloc- tricity price by only El, a substantial reduction from the current impact of E..0.
* Political Will is Crucial: Central banks far more likely to imple- ment climate risk management mea- sures in countries that ulready have strong national climate policies and where public opinion shows high con- cem about climate change.
Central bank actions are not signif- icantly associated with the country's 's economic exposure to transition risks, such as the size of its fossil fuel sector or the growth rate of its renewable en- ergy industry.
Economic Risks are Overlooked: There is no significant statistical link between the scale of a country's 's transition risks (such as exposure to stranded fossil fuel assets or the need for clean energy investment) and the actions taken by its central bank. * A "Risk Mitigation Gap" Exists: The disconnect hetween economic risk exposure and central bank action means that financial systems in coun- tries with high climate-relate eco- nomic risks but low political will are likely being left unprotected
More ambitious renewable deploy- ment yields even greater stability. In- creasing the deployment of solar and wind by 30% beyond the 2030 tar- gets would slash the sensitivity to gas prices even further. to less than E0.50 for every El increase.
Exposure to physical climate risks showed a statistically significant, though unexpected, positive correla- tion only with de-risking activities (promoting clean energy), not with re- risking (managing existing elimate/- fossil fuel risks).
* This price stabilization effect cre- ates tangible economic benefit for society, which the study terms an "insurance value." By buffering con- sumers and industries from fossil fuel price spikes, renewables provide a form of economic insurance.
Instead, central bank climate actions are significantly linked to the political context:
Countries with stronger national cli- mate policies tend to have central banks more engaged in re-risking fos- sil fuel investments and physical cli- mate impacts.
Countries with higher public concern about climate change tend to have cen- tral hanks more involved in de-risking clean energy investments.
This suggests central banks may be reinforcing national policy directions rather than acting independently as purely technocratic risk managers or compensating for weak national cli- mate policies.
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Table 33: Task 15 Outputs Across Input Scopes (Continued from Table 32
(i) Full Paper
(ii) Introduction Only
(iii) Abstract Only
Scientific Research Study Methods original
The findings are based dataset compiled by the rescarchers, documenting climate-relatee policies enacted by central banks across 47 Or- ganization for Economic Co-operation and Development (OECD) and Group of 20 (G20) countries up to August 2023. Policies were systematically classified as primarily "re-risking." "de- "de-risking"" or both. Composite scores for re-risking and de-risking ac- tivity were calculated for each coun- try, weighting policies based on their potential impact and cost. The re- searchers then used statistical linear re- gression analysis to test the correlation between these scores and various na- tional indicators, including measures of economic risk (fossil fuel scetor size, financial sector size, renewable energy growth, physical climate risk exposure) and political factors (cli- mate policy stringency index, public opinion data on climate concem). The analysis controlled for other country- level variables like GDP, central bank independence, and institutional man- dates.
The researchers conducted system- atic analysis of 47 central banks across the Organization for Eco- nomic Co-operation and Develop- ment (OECD) and Group of 20 (G20) nations. They created comprehen- sive new dataset of central bank ac- tions and developed novel classifi- cation system to distinguish between two key functions:
The findings are based on sophis- ticated computer simulation of the European power markets as they are projected to operate in 2030. The model incorporuted the energy infras- tructure and renewable capacity tar- gets outlined in Europe's National Energy and Climate Plans. To test the resilience of this future system, researchers subjected the model to the world historical variability of weather patterns, electricity demand, and volatile fossil fuel prices. The study introduced a new metric, "8- sensitivity." to precisely measure how much the average electricity price would change in response one- curo change in the price of natural gas.
1. Re-risking: Actions that integrate the financial risks fossil fuel invest- ments into standard risk management, discouraging "brown" assets, 2. De De-niising: Actions that reduce the investment risks associated with new clean energy technologies, facil- itating "green" assets. The study then statistically tested whether these ac- tions were more closely associated with measures of economic risk with indicators of political pressure, such as existing climate laws and pub- lie polling data.
Policy Implications
The research suggests that policymak- ers should expect central banks proactively fill gaps left by inad- equate national climate policy. partic- ularly concerning the risks of stranded fossil fuel assets. Key implications in- clude:
These findings suggest that policy- makers cannot assume central banks will act as autonomous, technocratic first responders to climate-related fi- nancial threats.
The "insurance value" of renewables a public good that is currently recognized or rewarded by market mechanisms. This leads to an under- valuing of renewable energy in invest- ment and policy decisions. To correct this, policymakers should: I. Explicitly factor the price- stabilizing benefits of renewables into policy and planning. Cost- benefit analyses for energy projects and support schemes should go he- yond direct generation costs to in- clude the economic value of reduced price volatility.
# National Governments Must Lead: Central banks tend to *reinforce* ex- isting national climate policy, not sub- stitute for it. To ensure financial sta- bility, governments must enact clear, strong, and credible climate and en- ergy transition policies. This pro- vides the political legitimacy central banks appear to require before they will act.
Central hanks tend to complement, not substitute: Central bank actions on climate risk appear influenced by, and aligned with, existing national climate policy strength and public sentiment. They are more likely to amplify exist- ing political will than to counteract its absence.
Potential risk mitigation gap: In countries with significant exposure to climate-related financial risks (espe- cially transition risks) but weak cli- mate policies or low public concern, central banks may not be taking suffi- cient action, leaving substantial risks unmanaged.
Do Not Depend on Central Banks to Fill Policy Gaps: Hopes that central banks could compensate for lack of political action on decarbonization are misplaced. Their role is more re- sponsive than proactive.
2. Pursue and potentially exceed cur- rent 2030 renewable energy targets. The research provides strong cco- nomic case that accelcrating the de- ployment of solar and wind power is direct investment in national eon- nomic stability and resilience against fossil fuel market shocks.
* Close the Risk Mitigation Gap: In jurisdictions with weak climate pol- icy, other financial regulators and government bodies must step in to ad- dress the unmanaged financial risks of the energy transition. Relying solely on the central bank in these situations could leave the economy vulnerable.
Transparency and international stan- dards may help: Increasing trans- parency about central bank climate ac- tions (e.g., via comparative indices) could create pressure on laggards. Fur- thermore, developing stronger inter- national standards or best practices through bodies like the Bank for In- ternational Settlements (BIS) or the Financial Stability Board (FSB) could encourage more consistent climate risk management across countries, re- gardless of domestic political head- winds.
3. Design market and support mech- anisms that recognize this insurance value. This could inform the struc- ture of renewable energy auctions other incentives, ensuring that the full socictal benefit of renewahles is cap- tured, leading to more efficient and secure energy transition.
* Clarify Central Bank Mandates: Governments can empower central banks by explicitly including the management of climate-related finan- cial risks within their official man- dates, thereby reducing ambiguity and concerns over "mission creep."
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I CROSS-DOMAIN GENERALIZATION TESTING
We test the model's generalization capacity across domains outside of its primary training focus. Due the scarcity of readily available peer-reviewed scientific paper-policy brief pairs in other domains, we conduct an exploratory evaluation using one representative paper from each of the following important fields: AI, Robotics, Mathematics, Economics, and Humanities. The generated policy briefs are then subjected to human evaluation. We test three models: Gemma-27B-SFF DeepSeek- V3, and GPT-4o. The resulting evaluation indicates that our supervised fine-tuning successfully generalizes to other domains.
We provide the details of 5 papers as follows:
AI: "Generative AI needs adaptive governance (Reuel and Undheim, 2024)" We show the results
Tables 34 and 35
Robotics: "Anticipatory policy design for robots in public spaces MMintrom et al. 2025
We
show the results in Tables and 37
Mathematics: "Educational policy to improve mathematics instruction at scale: Conceptualizing contextual factors (Ryve and Hemmi 2019)". We show the results in Tables and 39
Economics: "Financial inclusion, the shadow economy and economic growth in developing
economies (Younas et al., 2022)". We show the results Tables 40 and 41
Humanities: "Science cited in policy documents: Evidence from the Overton database (Fang
al. 2024)" We show the results Tables 42 and 43
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Table 34: AI: "Generative AI needs adaptive governance (Rcuel and Undheim 2024
Gemma-27B-SFT
(ii) DeepSeek-V3
(iii) GPT-4o
Policy Problem
Policy Problem
Policy Problem
Generative urtificial intelligence (Al) is developing with unprecedented speed, scale, and capacity to augment human work. Its ability create alistic text, code, images, and other content means is rapidly becoming peneral-purpose technology that will reshape economies and soci- cties. Existing cone-and-done reg- ulatory approaches cesthees--isllene disclosure, reg- istration, and fixed mak--maagement are ill-suited to this dynamic environment, Rigid rules risk ohso- lescence, misalignment, and stifling innovation, while leaving gaps for new harms to emerge. Policymakers need governance system that can keep pace with the technology, hal- ance agility with accountability, and incorporate new knowledge as comes available.
Generative Al poses unprecedented governance challenges due to its rapid development, broad societal impact, and ability augment hu- man cognition and creativity. Tradi- tional regulatory approaches-sturre slow-moving. and top-down-ane are ill- suited to address Al's dynamic risks (e.g., bias. misinformation, labor dis- ruption) and emergent capabilities. Policymakers face "pacing prob- lem," where rigid rules risk irrele- vance or misalignment with evolving technology.
The puper uddresses the societal and strategic challenge of effectively gov- erning generative Al technologies that evolve rapidly and impact hu- man performance, economic struc- tures, and socictal norms. Traditional governance systems struggle to keep pace with the transformative nature of these technologies, posing risks in regulation, societal impacts, and foundational governance structures.
Scientific Research Findings Key findings highlight that genera- tive Al. due to its rupid advancement and broad applicubility, challenges traditional governance frameworks. Existing regulatory strategies, which often static and inflexible, fail to accommodate the dynamic nature of Al development, leading poten- tial risks of regulatory misalignment and outdated policies. The paper proposes that adaptive goveraace,, which is flexible and evolves along with Al technologies, is necessary manage these challenges.
Scientific Research Findings Key insights from the paper include: Generative Al's rapid adoption, com- plexity, and high costs concentrate expertise oversight within indus- try, sidelining academia and civil so- cicty. Current governance (c-g-. the EU Al Act) relies fixed met- ries (c.g.. compute thresholds) that may quickly become obsolete al- gorithms improve. Adaptive gover- nance iterative, multi-taakeholder, succeeded
Scientific Research Findings
The authors demonstrate that gen- erative Al differs from previous AI generations in four key ways: it is more accessible, more complex, more expensive, and evolves faster. These characteristies create a "pac- ing problem" in which laws and reg- ulations cannot keep up. Drawing on environmental governance litera- ture, they show that "adaptive gov- cmaanc" charrcctriiid by ficxibil- ity. learning, and stakcholder co- evolution-is is essential for manag- ing such rapidly changing systems. They define an adaptive Al gover- nance framework that assigns roles to govemmentss industry, academia, civil society, and citizens, and out- shared activities (sharing best practices, collaborating, using sights. monitoring, informing, adapt- ing) and actor-specific activities (fi- nancing, anticipating, challenging. training, innovating).
and evidence-busee in domains like climate change and public health, offering model for Al.
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Table 35: AI: "Generative Al needs adaptive governance
(Reuel and Undheim 2024
(i) Gemma-27B-SFT
(ii) DeepSeek-V3
(iii) GPT-4o
Scientific Research Study Methods The study combines conceptual anal- ysis, literature review, and case study reasoning. It synthesises insights from Al development trajectories, in- vestment trends, research output, and public discourse. It draws envi- ronmental governance theory and ag- ile software development to articu- late the principles of adaptive gover- nance. The authors illustrate how adaptive framework could be opera- tionalised through examples such as sulti-stakkholler governance com- mittees embedded in government, structured policy review cycles, reg- ulatory sandboxes, and dedicated Al governance research funding. The EU AT Act is presented as a contrast case to show why static governance struggles with emergent foundation models.
Scientific Research Study Methods The puper synthesizes case stud- ies (c.g.. EU AI Act. COVID-19 responses) and proposes frame- work derived from environmental governance and agile methodolo- gies. It defines: Actors: Gov- ermments, industry, academia, civil socicty, and citizens. -Shared ac- tivities (SCUMIA): Sharing best practices, collaboration, monitoring, and adaptation. Actor-specific tasks (FACTI): Financing, anticipat- ing risks, challenging norms, training, and innovation.
Scientific Research Study Methods The rescarchers employed a mixed- methods approach, drawing from case studies, current policy anal- yses, and interdisciplinary frame- works. The study involved reviewing Al governance strategies across dif- ferent global entitics and theoretical frameworks from parallel fields like environmental and public health gov- emnanc. This comprehensive analy- sis informed the development of the proposed adaptive governance frame- work.
Policy Implications
Policy Implications
The findings suggest shift towards adaptive governance models is essen- tial for the effective regulation of gen- erative AI. Policymakers are encour- aged to adopt a dynamic approach that emphasizes flexibility, interactiv- ity, and rapid responsiveness. Such govemance should engage multiple stakcholders. including govermments, industry, academia, civil society, and citizens, a collaborative effort to continually refine and adapt policies to technological advancements and socictal needs. Adaptive governance can reduce risks, ensure Al's benefi- cial development. and maintain pub- lie trust. Additionally, investing in regulatory R&D and establishing bust monitoring and feedback pro- cesses will support the sustainability and effectiveness of Al governance frameworks.
1. Replace Static Laws with Iterative Processes: - Establish government-led "adaptive commit- tees" with rotating expert input to review and update regulations (c.g.. biannual revisions). Pilot regulatory sandboxes to test governance mea- sures controlled environments. Multi-Stakkehller Collabora- tion: Mandate cross-sector trans- parency (e.g., incident registries, compute/resounce disclosures) to in- forn policy.-Funi independent over- sight bodies (akin the IPCC) to assess risks and avoid regulatory cap- ture.
Policy Implications
1. Shift from static adaptive regulation: Adopt governance struc- tures that can be updated rapidly, drawing stakeholder input and new evidence.
2. Establish inclusive governance bodies: Embed permanent, cross- sectoral committees within govern- ment agencies.
3. Mandate iterative policy cycles: Design regulations with review peri- ods, pilot programmes, and revision pathways.
3. Capacity Building: Invest in public Al literacy and workforce training empower citizen engage- ment. Require corporate boards to include Al ethics officers and share risk assessments. 4. Global Coordination: Promote international standards for interoper- ability and safety, learning from nu- clear/biotech governance.
4. Invest in AI governance re- search: Fund independent Al safety and ethics research and ensure com- putational access for academia and civil snciety.
5. Promote tramsparency and ac- countability: Require interoperable risk frameworks and mandatory inci- dent disclosure.
Downsides and Mitigations Regulatory uncertainty: Provide clear roadmaps for phased policy up- dates.
6. Cultivate AI literacy: Educate
citizens to support democratic partic- ipation and responsible use.
Insufficient oversight: Layer third- party audits with government ac- countability.
Industry dominance: Fund public- sector Al R&D balance private in- fluence.
Analysis:
Gemma-27B-. SFF-SFFTTTTTT-- delivers the strongest policy-brief performance. It provides coherent problem framing, more detailed and conceptually accurate description of adaptive governance mechanisms, and significantly more actionable policy recommendations, DeepSeek-V3 V is detailed but verhose, and GPT-4o is clear but omits key theoretical and operational components. Gemma-27B-SFF therefore achieves the best balance of accuracy, clarity. and policy relevance.
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Table 36: Robotics: "Anticipatory policy design for robots public spaces (Mintrom al. 2025
(i) GemFm-27B-SFT
(ii) DeepSeek-V3
(iii) GPT-4o
Policy Problem
Policy Problem
Policy Problem
Robots are rapidly entering suretts, parks, malls, and civic infrastructure, creating major socio-technical tran- sition. Current governance remains overwhelmingly reactive, concentrat- ing on immeddate safety and privacy risks while neglecting westhetics, co- creation, equitable access, and long- term innovation trajectories, With- out anticipatory policy design that integrates citizen perspectives and broader social wellbeing goals, robot deployments risk uneven benefits, re- duced public trust, and missed oppor- tunities for positive transformation.
Robots are increasingly present in public spaces across major Asian cities, offering benefits like efficiency and convenience but also posing risks related to safcty, privacy, equity, and social disruption. Current policies in leading cities (Hong Kong, Scoul, Shanghai, Singapore, Taipei, Tokyo) primarily address safety and produc- tivity but often neglect broader soci- etal impacts, such as aesthetics, eq- uitable access, and participatory gov- ernance. Without anticipatory pol- icy frameworks, rapid robot adoption may exacerbate inequalities, erode public trust, create unintended harms.
The integration of robots into pub- lic spaces presents both opportuni- ties and challenges for society. While robots hold the potential to enhance productivity, convenience, and inno- vation, they also raise significant con- cerns around safety, privacy, ethics. and equitable access. Cities around the world, particularly Asia, are at the forefront of facing these chal- lenges as robot density increases. Without proactive and anticipatory risk
policy design, there negative social economic conse- quences, such as exacerbated inequal- ities and erosion of public trust.
Scientific Research Findings
A comparative review of six Asian cities (Hong Kong. Scoul, Shang- hai, Singapore. Taipei, Tokyo) shows that policies overwhelmingly em- phasize safety and, secondarily. pri- vacy. Broader concerns central to socio-technical transitions are incon- sistently addressed: only Scoul and Tokyo reference equitable access, and only Shanghai explicitly includes co co-creation. Aesthetics, distribution of productivity gains, and systemic innovation rarely appear despite their importance for public legitimacy and long-term value. This pattern reveals governance gap between techni- regulation and desired social out- comes.
Scientific Research Findings The research highlights that cur- rent policy frameworks in six Asian cities- Kong. Seoul, Shanghai. Singapore. Taipei, and Tokyo primarily focus on issues of safety and productivity. These citics have made varying degrees of progress in addressing privacy and ethics. systemic innovation, and equitable access. However, there is a distinct lack of emphasis on aesthet- ics and co-creation, which are crucial for fostering positive human-robor interactions and ensuring broader community acceptance.
Scientific Research Findings 1. Safety and Privacy: Policies uni- versally address physical safety but inconsistently cover data privacy and ethical risks (e.g., facial recognition biases).
2. Productivity vs. Social Costs: Rohots are promoted for economic gains, but their impuct on jobs, public space aesthetics, and human interac- tions is understudied. 3. Equity Gaps: Few cities (e.g.. Seoul, Tokyo) explicitly address eq- uitable access, risking exclusion of marginalized groups
4. Co-Creation Deficits: Only Shang- hai emphasizes co-design with citi- zens; most policies lack mechanisms for public input.
Innovation Focus: Systemic in- novation prioritized in tech devel- opment but not in ensuring inclusive governance or long-term socictal ben- efits.
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Table 37: Robotics: "Anticipatory policy design for robots in public spaces Mintrom et al. 2025
(i) Gemma-27B-SFT
(ii) DeepSeek-V3
(iii) GPT-4o
Scientific Research Study Methods The study systematically analyzes legislation, regulations, and strategy documents governing robots pub- lic spuces across six Asian cities. Us- ing seven-topic framework (safety. privacy/ethies, productivity. aesthet-
Scientific Research Methods
Scientific Research Study Methods The study employed a comparative analysis of existing public policy and regulatory settings across six major Asian cities. By reviewing legisla- tion, regulations, and strategic docu- ments, the researchers assessed these cities using seven-topic analyti- cal framework: safety, privacy and ethics, productivity, aesthetics, en- creation, equitable access, and sys- temic innovation. The methodology involved thorough policy searches us- ing government websites and trans- lating policy documents where neces- sary to ensure comprehensive analy- sis.
The study analyzed 30 policy docu- ments across six high-robot-density Asian cities using seven-topic
framework:
1. Safety
2. Privacy/Ethics
ics, co-creation, equitable access, sys- temic innovation), each policy doc- ument was assessed using three- tier scoring system: directly ad- dressed, signalled, not mentioned. Documents were translated when necessury. and comparative narra- tives were produced to reveal cross- jurisdictional patterns and gaps.
3. Productivity
4. Aesthetics
5. Co Co-Creation
6. Equitable Access
7. Systemic Innovation
Policies were scored based on ex- plicit attention to each topic. supple- mented by media analysis and cross- jurisdictional comparisons.
Policy Implications
Policy Implications
1. Expand Policy Scope: Move beyond sufety/productivitty to address ethics (c-g., bias audits), aestheties. and equitable access.
Policy Implications
Embed unticipatory governance tools such as foresight exercises, horizon scanning, and iterative policy review cycles.
The findings suggest that anticiputory policy design is essential for maxi- mizing the benefits and minimizing risks associated with robots public spaces. Policymakers should focus developing inclusive design processes that broaden stake- holder participation. including di- resident voices. Engagement proactive policy learning and innova- tion diffusion across jurisdictions can lead to more effective management of sncio-technical transitions. By prioritizing co-creation and westhet- ies alongside established considera- tions like safety and productivity, pol- icymakers can foster environments where robots contrihute to social and economic wellbeing. Expunding an- ticipatory efforts to incorporate these neglected areas will help ensure that the integration of robots into pub- lic spaces preserves public trust, en- hances community experiences, and promotes equitable access to techno- logical advancements.
Co- Design with Citizens: Adopt participatory methods (e.g., citizen juries, design labs) to shupe robot deployment rules.
Expand policy scope beyond safety and privacy explicitly address aesthetics, co-creation, equitable access, and systemic in- novation.
3. Equity Safeguards: Mandate accessibility features (e.g.. for disabilities) and subsidize robot services in underserved areas.
co-design through participatory workshops, neighbourhood pilots, and continuous citizen feedback loops.
Institutionalize
4. Anticipatory Governance: Es- tablish regulatory sandhoxes test robot impacts and update policies iteratively.
Promote inclusive innovation via regulatory sandhoxes, re- search incentives. and data- sharing mechanisms.
5. Cross-City Learning: Create platforms for Asian cities to share best practices on inclusive robot governance.
Encourage international and cross-city learning, drawing on practices in Scoul, Tokyo, and Shanghai.
Conclusion
Asian cities are pioneers in robot deployment but must shift from re- active anticipatory policymaking. Prioritizing co-design, equity, and ethical oversight will ensure robots enhance- not undermine- being. Policymukers should treat robots as social, not just technologi- cal, innovations.
well-
Analysis:
Gemma-27B- provides the strongest brief. captures the full socio-technical transition outlined the paper, integrates all seven analytical topics, and links findings to concrete anticipatory governance mechanisms. DeepSeek-Vy V3 is comprehensive but more descriptive and repetitive. GPT-4o is clear but omits key cross-city contrasts and understates co-creation and equity dimensions. Gemma-27B-SHP offers the best balance of accuracy, breadth, and policy relevance.
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Table 38: Mathematies: "Educational policy to improve mathematics instruction at scale: Conceptualizing
contextual factors (Ryve and Hemmi
2019
(i) Gemma-27B-SFT
(ii) DeepSeek-V3 V3
(iii) GPT-40
Policy Problem
Pollcy Problem
Policy Problem
Large-scale improvement of mathe- matics instruction in Sweden is hin- dered by a persistent misalignment between national educational policies and actual classroom practice. De- spite strong teacher autonomy and progressive intentions, reforms fre- quently fail achieve meaningful in- structional change because they account for three key contextual features: teachers' system-level au- tonomy, their traditionally reactive role in classrooms, and the long- standing culture of invisible peda- gogy that favors implicit guidance over explicit instructional leadership. Without policies that directly address these contextual constraints, high- level initiatives risk limited uptake and minimal impact.
The paper addresses the challenge of improving mathematics instruction at scale, particularly within specific edu- cational contexts like Sweden. A key issue is gap between educational policies and their effective implemen- tation classrooms, influenced by both explicit factors (c.g.. curriculum, teacher training) and implicit contex- tual factors (e.g.. teacher positioning. pedagogical traditions). Policymak- and educators often struggle to ac- count for these contextual elements, leading to mismatches between pol- iey intentions and classroom realities.
The educational system faces criti- cal challenge in improving mathemat- ics instruction at large scule. Tra- ditional methods have often failed to account for the varied contextual fac- tors present in different educational environments, resulting in ineffective policy implementation. Addressing these challenges kcy to enhancing student outcomes in mathematics.
Scientific Research Findings The study highlights criticul contex- tual factors affecting mathematics in- struction at scale, including: 1. The positioning of teachers within the ed- ucational system, which is influenced by empowerment versus control poli- cies. 2. The role of teachers within the classroom, where proactive en- gagement in setting learning goals and orchestrating class discussions is essential. 3. Traditions visible ver- sus invisible pedagogy, influencing how educational strutegies are imple- mented and perceived.
Scientific Research Findings
The study identifies three critical * *implicit contextual factors** that shape the success of large-scale ed- ucational reforms mathematics: I. Positionnng of Teachers in the Educational System In Swe- den, teachers have significant auton- omy, but this can lead to variabil- ity in instruction and lack of co- hesive implementation cies. 2. e Positioning Teachers in the Classroom** ** Swedish teachers are often reactive rather than proac- tive, focusing student-led leurning without structured teacher guidance, which may hinder deep mathemati- cal understanding. 3. **Traditions of Visible vs. Invisible #edagogy Swedish education emphasizes "in- visible pedagogy." with minimal ex- plicit instruction and weak framing of learning objectives, making it dif- ficult to align reforms with existing norms.
Scientific Research Findings
Evidence from the five-year "Count on Vasteras" project shows that sys- temic instructional improvement de- pends on recognizing three interre- lated contextual factors: (I) Swe- den's empowerment-orienee educa- tional system positions teachers with high autonomy but limited structural support for sustained improvement; (2) classroom norms position teach- reactive helpers rather than proactive instructional leaders; and (3) invisible pedagogy, characterized by weak curriculum framing und im- plicit expectations, obscures core in- structional work. When reforms in- troduced new roles, explicit instruc- tional tools, structured collegial learn- ing events, and routines that made pedagogy visible, teachers reported greater clarity, improved collabora- tion, and enhanced student learning outcomes.
new poli-
Key empirical insights: Existing professional development (PD) of- ten fails to address these contextual factors. Teachers appreciate struc- tured tools (e.g., the +*Five Prac- tice Model when they empower rather than dictate practice. Text- book reliance and weak lesson plan- ning norms hinder systemic change.
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Table 39: Mathematics: "Educational policy to improve mathematics instruction at scale: Conceptualizing
contextual factors (Ryve and Hemmi 2019)".
(i) (eemma-27B-SFF
(ii) DeepSeck-V3
(iil) GPT-4o
Scientific Research Study Methods The study applies educational de- sign research over 2012-2017 across 38 schools (about 400 teachers and 10,000 students). Guided by Cobb and Jackson's 's learning design frame- work, researchers collected longitu- dinal teacher surveys, municipality- wide student assessments, textbook and syllabus analyses, classroom ob- servations, and interviews with teach- ers and teacher educators. These data informed iterative refinements to professional development materi- als, new instructional tools, new orga- nizational routines, and new teacher- support roles.
Scientific Research Study Methods The study employed mixed- methods approach within the "Count project (2012-2017). a large-scale reform effort in Sweden:
Scientific Research Study Methods This research builds on theoretical frameworks and empirical studies conducted during the "Count Vasteras" project, which involved col- lahoration with Swedish schools. Uti- lizing educational design research, the study iteratively developed strate- gies by analyzing teacher surveys, student math tests, and textbook con- tent, alongside interviews and obser- vational studies. This approach re- vealed patterns and deeper insights into the contextual factors impacting educational policy implementation.
Vasteras" (CoV)
Surveys & Interviews: Pre-, mid-term, and post-intervention questionnaires from -400 teachers to assess perceptions of PD and classroom practices.
Student Assessments: Yearly tests to measure mathematical proficiency across - 10.000 students.
Textbook & Policy Analysis: Examination of Swedish curriculum materials and teacher guides to iden- tify gaps in support for teacher- led instruction.
Policy Implications
To improve mathematics instruc- tion on a large scale, policymakers should:
Policy Implications
1. Embed new instructional leader- ship roles, such as Mathematics Men- tors and Heads of Mathematics, in ways that align with Sweden's em- powerment culture.
Iterative Design Research: Ad- justments to PD programs based on real-time feedback and contextual barriers.
Recognize and integrate the em- powerment teachers within policy frameworks, allowing them flexibil- ity and agency in instruction while providing clear, supportive tools and strategies.
2. Introduce explicit instructional tools. lesson- pessonnllannin supports, and formative assessment routines that make teacher work visible and strengthen proactive classroom lead- ership.
Policy Implications 1. Adapt PD Programs to Teacher Autonomy- Reform efforts should empower teachers by providing frameworks (e.g.. goal-setting tools) rather than rigid scripts. respecting their professional ageney while guiding instructional improvement. 2. Strengthen Teacher Leadership Introduce roles like "Heads of Mathematics" to facilitate peer col- laboration and bridge policy-practice gaps.
Facilitate the active engagement of teachers in the classroom by introduc- ing frameworks that require strategic planning and the proactive orchestra- tion of classroom activities.
3. Preserve teacher autonomy while providing clearer learning goals, structured supports. and coherence across curriculum materials.
Balance invisible pedagogical tradi- tions with explicit instructional strate- gies that clearly define teacher roles and responsibilities. Implementing visible pedagogies can improve ac- countahility and learning outcomes.
4. Require all large-scale reforms to begin with systematic contextual analysis of teacher norms, classroom practices, and pedagogical traditions. 5. Build distributed instructional ex- pertise through networks of school- level leaders who can adapt tools to local conditions and sustain reforms over time.
3. Make Pedagogy More Visible Shift toward structured, explicit teaching strategies (e.g.. problem- solving frameworks) while retaining student engagement elements.
4. Context-Sensitii Policy Design Policymakers should analyze implicit cultural factors (e.g., teacher positioning. pedagogical norms) hefore scaling interventions.
Foster Cross-System Learn- ing Promote exchange between researchers, teachers, and policymak- to iteratively refine reforms bused local realities.
Analysis:
Gemma-Gemmm-22 Gemma- SFT provides the strongest policy brief. It offers a clearer synthesis of the three contextual factors, relates them directly to the mechanisms of instructional improvement, and presents actionable, context- aware policy recommendations aligned with the paper's evidence. DeepSeek-V3 is informative but more descriptive and diffuse, and GPT-4o is clear but omits several critical mechanisms, Overall, Gemma-27B-SFT achieves the best balance of accuracy, structure, and policy relevance.
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Table 40: Economics: "Financial inclusion, the shadow economy and economic growth in developing
economics (Younas et al. "022
(i) GemMa-27B-SFT
(ii) DeepSeck-V3
(iii) GPT-40
Policy Problem
Policy Problem
Policy Problem
Financial exclusion and widespread informal cconomic activity under- mine sustainable economic growth in developing countries. Low access to formal banking, credit, and sav- ings systems reduces investment and productivity. while large shadow economy constrains revenue collec- tion, weakens institutions, and lim- its the reach of macroeconomic pol- icy. Policymakers require guid- ance on how financial inclusion and shadow-ecooomy dynamics jointly shupe growth prospects.
Developing cconomics face signifi- cant challenges achieving sustain- able economic growth due to limited access formal financial services and large shadow economy (infor- mal cconomic activitics unreported to authoritics). Financial exclusion and informal economic activities hin- der productivity, tax revenues, and equitable development, exacerbating poverty and inequality. Policymak- ers need evidence-based strategies to promote financial inclusion and curb the shadow cconomy to foster inclu- sive growth.
Developing economics face two- pronged challenge impacting their economic growth: low levels of financial inclusion and a substan- tial shadow economy. These factors collectively impede invest- ments, resource allocation, and socio- economic ad advancements. crucial for sustainable development in these re- gions.
Scientific Research Findings Recent research spanning from 2008 2017 illustrates that financial inclu- sion significantly enhances cconomic growth in developing cconomies. Conversely, the shadow economy de- tracts from economic performance. posing challenges for systemic in- tegration into formal financial sys- tems. Notably. there is bidirec- tional causality between financial in- clusion and economic growth, well as between shadow economy activi- ties and economic growth.
Scientific Research Findings Using panel of 30 developing economies 2018). the study finds:
Scientific Research Findings The study analyzed data from 30 developing economies (2008-2017) and found:
Financial inclusion strongly and consistently increases GDP per capita across all econometric specifications.
Financial inclusion (measured by access banking services, credit, and savings) has positive and statistically significunt im- pact on economic growth (GDP per capita). A 1% increase in fi- nancial inclusion hoosts growth by 15,9e on average.
Shadow economy size has large negative effect on GDP per capita in most regions.
Bidirectional Granger causality exists between financial inclusion and economic growth, tween shadow cconomy size and economic growth, indicating mutually reinforcing dynamic.
The shadow economy negatively affects growth: 1% increase informal economic activity reduces GDP per capita by 53.6%. Regions with larger shadow economies (e.g., Latin America, Sub-Saharan Africa) ex- hibited slower growth.
These relationships vary in mag- nitude across regions, reflecting institutional and structural differ- ences.
Bidirectional causality exists: Economic growth also drives fi- nancial inclusion, while larger shadow economy stifles formal fi- nancial sector development.
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Table 41: Economics: "Financial inclusion, the shadow economy and economic growth in developing
economics (Younas al. 2022
(i) Gemma-27B-SHP
(ii) DeepSeek-V3
(iii) GPT-4o
Scientific Research Study Methods
Scientific Research Study Methods Data Sources: World Bank Develop- ment Indicators, IMF Financial Ac- cess Survey, and Medina Schnei- der's shadow economy database. Methodologies: Panel regression models (fixed effects) isolate re- gional impacts. Dynamic panel analysis (Arellano-Bond GMM) to address endogeneity lagged ef- fects. Granger causality tests to confirm bidirectional relationships. Variables: GDP per capita (de- pendent). financial inclusion index. shadow economy size (% of GDP), and controls (inflation, trade, unem- ployment).
Scientific Research Study Methods The study utilized data from the World Development Indicators, IMF's Financial Access Survey, and Medina and Schneider's glohal database across 30 developing cconomics. Employing panel data analysis, the researchers conducted panel ordinary least square (OLS) fixed effect, two-step differenced generalized method of moments (GMM), and panel Granger causality approaches derive robust findings.
The authors use harmonized panel data for 30 developing economies (2008-2017) and apply three comple- mentary appraches:
A fixed-effects panel OLS model to estimate short-run relation- ships while controlling for time- invariant heterogencity.
A two-step difference GMM esti- mator address endogeneity, dy- namic adjustment, and serial cor- relation.
A Dumitrescu-Hurtie punel Granger causality test to examine directional relationships among financial inclusion, shadow economy size, and economic growth.
Policy Implications
These insights advocute for poli- cies that enhance financial inclusion through increased access to banking services, credit facilities, and finan- cial literacy programs, Simultane- ously, strategics to shrink the shadow economy, such as improving regula- tory frameworks and reducing cor- ruption, are critical. By dning so, de- veloping economies can foster eco- nomic stability and growth. This dual approach not only addresses im- mediate financial access challenges also strengthens long-tern term-termm eco- nomic structures, helping transition- ing economies achieve broader devel- opment objectives.
Policy Implications
1. Expand Financial Inclusion: Digital financial services: Leverage mobile banking and fintech to reach unbanked populations. - Affordable credit: Support microfinance and SME lending to integrate informal businesses into formal sector. Financial literacy programs: Edu- cate citizens on using formal finan- cial tools.
Data come from the World Development Indicators, IMF Financial Access Survey, and the Medina-Schneida shadow economy database.
Policy Implications
Expand financial inclusion: Promote access bunking. credit, mobile money, and sav- ings tools; reduce intermediation costs; and strengthen digital financial infrastructure.
Reduce Shadow Econ- omy: Simplify tax systems: Lower compliance burdens to incentivize formalization. Strengthen gover- nance: Combat corruption and im- prove transparency to rebuild trust institutions. Labor market reforms: Provide social protections to reduce reliance informal work.
Shrink the shadow economy: Simplify regulatory require- ments, improve tax enforcement, strengthen rule of law, and reduce corruption to incentivize formalization.
3. Regional Tailoring: Policies should address regional disparities (e.g.. higher shadow economies in Latin America vs. lower financial inclusion in Sub-Saharan Africa).
Coordinate reforms: Because fi- nancial inclusion, shadow econ- omy activity, and growth rein- force each other, policies should be integrated rather than isolated.
Tailor regional strategies: Dif- ferences in institutional quality and economic structure require region-specific approaches.
Analysis:
Gemma-27B-SFFF produces strongest policy brief. offers the clearest causal explanation, integrates all most actionable and region-sensitive policy recommendations, DeepScck- is comprehensive but more verbose, and GPT-40 concise omits methodological and regional detail. Gemma-27B-SB achieves the best balance between clarity, completeness, policy relevance.
empirical findings, and presents the
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Table 42: Humanities: "Science cited in policy documents: Evidence from the Overton database (Fang
et al. 2024
(i) Gemma-27B-SFT
(ii) DeepSeek-V3
(iii) GPT-4o
Policy Problem
Policy Problem
Policy Problem
Despite growing calls evidence- based policymaking, remains un- clear how extensively scientific search used policy documents and which fields. publication types, and research topics policymakers rely most. Without systematic under- standing of this uptake, goveruments and research systems cannot assess the real-world influence of science, allocate funding effectively, design strategics that increase the policy rel- evance of research outputs.
Bridging Science and Policy: In- sights from Overton Database on Research Utilization in Policymaking The translation of scientific research into actionable policy is a critical challenge for governments and orgu- nizations. Policymakers often strug- gle to identify and integrate rele- vant scientific evidence into decision- making due to the sheer volume of re- search and dispurities in its policy rel- evance. This study addresses the gap in understanding how often and in what ways scientitic publications are cited in policy documents, revealing patterns that could improve evidence- based policymaking.
Utilizing Scientific Research in Pol- iey: Examining Evidence from the Overton Dutabasc
The intricate relationship between scientific research and policy devel- opment presents socictal challenge: ensuring that scientific findings effec- tively inform policy decisions. This issue closely relates to understanding how scientific publications uti- lized within policy documentation. a key step in enhancing evidence-based policymaking.
Scientific Research Findings
Scientific Research Findings
Matching 18.3 million Web of Sci- ence publications (2010-2019) with Overton's 's global databasc of policy documents shows that only 3.9% of publications are cited at least once in policy texts. Policy citations are delayed: publications from 2010 have 6.47% coverage, while 2019 publications show only 0.78%. Re- views (8.2% coverage) and articles (4.4%) attract far more policy tention than other document types. Across fields. Social Sciences and Humanities lead (12.34% coverage), followed by Life and Earth Sciences (5.96%) and Biomedical and Health Sciences (5.72%). Physical Sciences and Engineering (0.62%) ) and Math- ematics/Computer Science (0.93%) are seldom cited. Within each major field, policy attention is uneven at the micro-field level, indicating selective relevance of specific research topics.
The study indicates notahly limited use of scientific publications in policy documents, with only 3.9% of the million analyzed publications being cited at least once. Social Sciences and Humanities (SSH) research re- ceived the most citations from policy documents, with 12.34% coverage, followed hy Life and Earth Sciences and Biomedical and Health Sciences. fre-
Scientific Research Findings Only 3.9% million scientific publications (2010-2019) were cited policy documents, with an average 0.09 citations per paper. Policy citations show time lag: Older pub- lications (c.g.. from 2010)
times more likely to cited than recent ones (e.g., 2019). Document types matter: Reviews (8%) and articles (4.4%) most cited, suggesting pol- icymakers favor synthesized prehensive research. Field dispar- ities: Social Sciences and Human- ities (SSH): Highest policy engage- ment (12.3 1..3% cited). Life/Earth Sci- ences (LES) and Biomedical/H Sciences (BHS): Moderate engage- ment 6% cited). Physical Sci- ences/Ennginerring (PSE) and Math- ematics/Computer Science (MCS): Low engagement (<1% 1%% cited).
Reviews and articles are quently cited, suggesting tendency towards comprehensive and synthe- sized research ourputs.
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Table 43: Humanities: "Science cited in policy documents: Evidence from the Overton database (Fang)
et al. 7
(i) GemFF-27B-SFT
(ii) DeepSeek-V3
(iii) GPT-4o
Scientific Research Study Methods The study integrates 18.3 million WoS publications with Overton's s global database of policy documents using DO1 matching (June 2020 snap- shot). Subject classification is per- formed with the CWTS publication- level system, mapping papers to 4,013 micro-fields and five macro- fields. Three indicators quantify pol- icy uptake: Coverage (share cited), Density (citations per publication), and Intensity (citations per cited pub- lication). VOSviewer visualizations highlight topic clusters receiving dis- proportionate policy attention.
Scientific Research Study Methods Data source: Analyzed 18.3 mil- lion Web of Science publications (2010-2019) matched with policy ci- tations from Overton. global policy document database. Metries: Mea- sured policy impact using: Cover- age (% of papers cited), Density (average citations per paper), In- tensity (average citations per cited paper). FFeldd classification: Used the CWTS/Leiden Ranking system to categorize publications into five broad fields (e.g.. SSH, BHS). - Visu- alization: Employed VOSviewer to map citation patterss across rescarch topics.
Scientific Research Study Methods This analysis utilized the Overton database, the world's largest assem- bly of policy documents, to track ci- tations of scientific publications in- dexed by the Web of Science from 2010 to 2019. The researchers em- ployed bibliometric analysis and vi- sualization tools like VOSviewer to assess citation patterns, focusing on coverage, density, and intensity of policy document citations across pub- lication years, document types, and scientific fields.
Policy Implications
The findings highlight potential strategics to enhance the utilization of scientific research in policymak- ing. Emphusizing review articles and fostering interdisciplinary research, particularly in areas such SSH and environmental sciences, could in- crease policy engagement. Policy- makers should consider these fields' relevance and contribute to reduc- ing the delay in the uptake of recent scientific findings. Furthermore, ad- dressing the underrepresentation of certain fields in policy documents can optimize research impact and foster a more halanced approach to evidence- based policy development.
Policy Implications
Funding strategies should consider the strong policy pull of SSH. BHS and LES relative PSE and MCS.
Policy Implications
1. Targeted Research Funding: Prior- itize funding for SSH, LES, and BHS fields, which show higher policy rel- evance, but ensure intendisciplinary integration to address complex soci- etul challenges. 2. Timeliness of Evidence: Policymakers should ac- count for the citation delay (3+ years) when seeking evidence, or invest in systems to flag emerging high-impact research. 3. Support for Review Arti- cles: Encourage synthesis of research (c.g., systematic reviews) to bridge gaps between academia and policy. as these are highly cited. 4. Database Improvements: Expand policy cita- tion tracking to include non-DOI pub- lications and non-Fnglish research to reduce bias in visibility. 5. Evalua- tion Metries: Incorporate policy ci- tations (e.g.. g.. via Overton or Altmet- ric) into research assessment frame- works incentivize policy-rrlevant science.
Reviews and articles dominate pol- icy citations, indicating that policy- makers rely heavily on synthetized and peer-reviewed evidence. Policy citation metrics accumulate slowly: evaluation frameworks should use longer assessment windows for pol- icy impact. * Comprehensive policy- document indexes like Overton are essential infrastructure; broader cov- erage and openness would enhance research- research-policy tracking. Field- level inequalities suggest the need for targeted knowledge- www.lege-translation initia- tives to support underrepresented dis- ciplines in reaching policy audiences.
Analysis:
Gemma-27B-SHF provides the most policy-ready and analytically complete brief. It incorporates specific numerical findings, interprets field-level disparities, highlights micro-field variation, and links evidence to clear policy levers. DeepSeck-V3 V3 is detailed hut more narrative and less structured, while GPT-4o offers concise delivers the strongest balance
overview hut omits several central quantitative insights. Overall, of accuracy. completeness, and actionable relevance.
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J DETAILS OF 85 EXPERT-WRITTEN PAPER-BRIEF PAIRS
We provide the detailed list of 85 pairs in Appendix a.n and their yearly distribution in Appendix J.2 We also provide the detailed list of 3 new pairs for in-context polishing in Appendix J.4 and new pairs for Task 15 in Appendix J.3
J.1 LIST OF 85 EXPERT-WRE WRITTEN PAPER-BRUE PAIRS
We document the 85 expert-written scientific paper-policy brief pairs included in Sci2Pol-Bench, citing each source to ensure transparency and reproducibility.
1. Paper (Link et al. 2025a) (DOI: 10.1088/441000-025-011111
Brief (Link et al.
2025b
(DOI:
10. 10111008841500--- 013-660002--11 015-117411 (17741-9;;; Nature Energy: Accessed: 2025-05-10.
2. Paper (Shears et al. 2025a) (DOI: 10. 000000//41150000550022222
Brief (Shears et al.
20256)
(DOI: 10.1038/$41500 005000000122222
Nature Energy; Accessed: 2025-05-
3. Paper (Navia Simon and Diaz Anadon 2025b) (DOI: 10.10000/41
Brief
(Navia Simon and Diaz Anadon 2025a)
(DOI: 000.0.00//1111500050005555555550
Nature Energy:
Accessed: 2025-05-10. 10.
4. Paper (van Heerden et al. 2025a) (DOI: 10. 100.100804411500055-0100---1 Brief (van Heerden et al.
2025b) (DOI: 10. 1038/ (500-02--0122--z) Nature Energy; Accessed: 2025-05-10.
5. Paper (Millinger et al., 2025b) 2025a) (DOI: 10. 10.10/8//44 00000/4441100000-6666666-66
(DOI: 10. 10.1033844415600222--6666--6- Brief (Millinger et al.
Nature Energy; Accessed: 2025-05-10.
6. Paper Odenweller and Ueckerdt 2025b) (DOI: 100100044444550--4444006-------- Brief (Odenwellrr
and Ueckerdt 2025a)
(DOI: 10.10008/44400000020000000
Nature Energy; Accessed: 2025-05-
10.
7. Paper (Caggiano al. 2024b) (DOI: 101108844110000004000000003 Brief (Caggiano al.
2024a) (DOI:
Nature Energy: Accessed: 2025-05- 10.
8. Paper (0 SSShaggnessyy et al. 2024b) (DOI: 10.10088/411550044401555566 Brief (0 (haShaugyysssy
et al. 2024a) 10.100844411664044055555--/ Nature Energy; Accessed: 2025-05-10.
9. Paper (ennedy et al. 2024a) (DOI: 10:1080/411560022405550--w) Brief (Kennedy et al. 2024b)
(DOI: 10.1038/4415000044-0000
Nature Energy; Accessed: 2025-05-10.
10. Paper (Sitarz et al.
2024a) (DOI: (0.1033/44150000240055555552 Brief (Sitarz et al. 2024b)
(DOI:
10. 10.1038/411500024411555333
Nature Energy; Accessed: 2025-05-10.
11. Paper (Link et af.
20245) (DOI:
Brief (Link et al.
2024a
(DOI:
10. 10.10387/41150-044-111
Nature Energy: Accessed: 2025-05-10.
12. Paper (White et al.) (DOI: 10.1038//41550002300144444
2024a) (DOI: 10. 10.1038/4415000033004-22222 Brief White et al.
2024b)
Nature Energy; Accessed: 2025-05-10 10.
13. Paper WWllkee et al. 2023b) (DOI: 10. 1000000411500002--1299----- Brief (Wolske et al. 2023a)
(DOI: 101008.4415000020001322
Nature Energy; Accessed: 2025-05-T0.
14. Paper Deshmukh et al. al., (2023)) (DOI:
Brief (Deshmukh et al.
(2023)) (DOI: 10. 1111033/-1150--02-00222
Nature Energy; Accessed: 2025-05-10 10.
15. Paper (Gars et al. 2022a) (DOI: 10.
122-6); Brief (Gars et al.
2022b) (DOI:
10.1038/441500022001123
Nature Energy: Accessed:: 2025-05-10.
16. Paper Gruber et al. 2022a) (DOI: 10.1038/-415600022-0099999
Brief (Gruber al.
2022b
(DOI: 00.1088//4410000222000033
Nature Accessse 2025-05-10.
17. Paper (Longden lo 2022a (DOI: [0.1038:441500000-00092222 Brief (Longden et al.
2022b
(DOI: 10:003844115000221499966666 Nature Energyy 2025-05-10.
18. Paper (Pachurri et al. 2021a) (DOI: [0.10088/411500002-000011333
Brief (Pachauri et al.
2021b)
(DOI: 10.10%%/4445000000000099999
Nature Energy;; 2025-05-10.
19. Paper (Hall et al. 2021b) (DOI: 10.10386411500001-00778111 Brief (Hall al. 2021a
(DOI:
10.1038/41150000200000000 -0W2:1-w Nature Accessed: 2025-05-10.
20. Paper Kitzing et 2020a) (DOI: 10. 00.1088/441500000-007777--- Brief (Kitzing et al.
2020b)
(DOI: 10.003
Nature Energy: Accessed 2025-05-T0.
21. Paper (Bonan et al.) 2020b) (DOI: 10. 1033//415500000-007799--- Brief (Bonan et al. 2020a)
(DOI:
Nature Energy; Accessed: 2025-05-10.
22. Paper (Goldstein et al.) 2020a) (DOI: 10. 101.118//41550000000666666
Brief (Goldstein et al.
2020b) (DOI:
Nature Energy: Accessed: 2025-05-10.
23. Paper (Mani et al. 2020a) (DOI: 01166//41560-020-5555 Brief (Mani et al. 2020b) (DOI:
10.
Nature Energy; Accessed: 2025-05-10.
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24. Paper (Casey et al. 2020b) (DOI:
10.1038/4411500002--000000 Brief (Casey et al. 2020a) (DOI:
10. 10.00///4156000200062222
Nature Energy; Accessed: 2025-05-10.
25. Paper Kontokosta et al. (0200a) (DOI:
Brief (Kontokosta et al.
2020b) (DOI: 10.100366115000000000000000 1500-000-000-3->>
Nature Hnergy; Accessed: 2025-05-10.
26. Paper (Kaufmann and Connelly 2020b) (DOI: 10. 100110008/441500000000444444
)); Brief (Kaufmann)
and Connelly 2020a) (DOI: 10.0008//4155000000055--------00 Nature Energy; Accessed: 2025-05-
10.
27. Paper (Braunholtz-Speight et al. (0000000000011110001100000000000 Brief (Braunholtz
Speight et al. (2020b) (DOI: 0el.::0//41155000000555-------21 Nature Energy; Accessed: 2025-05-10 10.
28. Paper (Kar et al. 2019) (DOI: 10. 10.100886415000019-0-4222
Brief (Kar et al. 2020 (DOI:
10.
Nature Energy; Accessed: 2025-05-10.
29. Paper (Liu and Rajagopal| 2019) (DOI: (0.1038//41500-01999000
Brief (Rajagopal and Liu
2020) (DOI: 10. 00638/44560001--05333
Nature Energy: Accessed: 2025-05-10 10.
30. Paper (White and Sintov 2020a) (DOI: 10.108//411500019-00000
Brief (White and Sintov
2020bb (DOI: 10.10///s4 00988/441500-111 1550001--0555--100 Nature Energy, Accessed: 2025-05-10.
31. Paper (Rinscheid and Wüstenhagen 2019b) (DOI: 10. 100000884416000011.00006-)))) Brief (Rinscheid
and Wüstenhagen 2019a) (DOI:
Nature Energy; Accessed: 2025-
05-10.
32. Paper (Mays et al. 2019a) (DOI: 10. 10.100364411500-0994447766666660 Brief (Mays et al.
2019b
(DOI:
10. 10.1038/s41560-0 9-0502-33); Nature Energy; Accessed:: 2025-05-10.
33. Paper (Egli et al., 2018) (DOI: 10. 10.1033/4411600018--777
Brief (Egli ct al.
2019) (DOI:
10. 10387/415000019-4664
Nature Energy; Accessed: 2025-05-10.
34. Paper (Tiefenbeck et al.) (019b) (DOI: 10.1038/541100-018--222
8-0282-11)) Brief TTefenbecc et al.
2019a) (DO1: 1038/44 00003///11560019990 1500001-00400-5);; Nature Energy; Accessed: 2025-05-10.
35. Paper (Azarova et al. 2018) (DOI: 10.1033/s41 1000//415000011-0005---- Brief (Azarova et al. 2019)
(DOI:
Nature Energy, Accessed: 2025-05-10.
36. Paper (Apostoleris et al. 2018) (DOI:
0008//415000018-02555555
Brief (Apostoleris et al.
2019) (DOL: 10.1000/441166660 001-100000-111------4 Nature Energy: Accessed: 2025-05-10.
37. Paper (Mahdavi et all 2025a) (DOI: (0010888/415588-055-222228333383 Brief (Mahdavi et al. 2025b)
(DOI: 141008//441550-005522244444
Nature Climate Change; Accessed: 2025-05-10 10.
38. Paper (Ogier et
2025b) (DOI:
1-4); Brief (Ogier
2025a
(DOI:
1038//41158-005-2220200
Nature Climate Change; Accessed: 2025-05-10.
39. Paper (Liu et al.
20256) (DOI: 10. N0.1038//415588 050--15----22-277--22 Brief (Liu al.
2025ch
(DOI:
10. 10.1038/s41558 004444115--02--2244450 024-02240-7) Nature Climate Change; Accessed: 2025-05-10.
40. Paper (Tang et al. 2024a) (DOI: 10.1038/<41555-022-0000 558-0244021122-4); Brief (Tang et al. 2024b)
(DOI:
10. (033/641558-002-021444
Nature Climate Change; Accessed: 2025-05-10.
41. Paper (Druckenmiller et al. (022a) (DOI: 10. 155-09441158-22-000022222
Brief (Druckenmiller Nature Climate Change; Accessed: 2025-05-
et al. 2024b) (DOI: 10.
42. Paper Nowak et al. 2024b) (DOI: 10. (DOI: 10.1038/441158.024--000
Brief (Nowak et al.
2024a)
Nature Climate Change; Accessed: 2025-05- 10.
43. Paper (Lamb al. 2024a) (DOI: (0011038//41158--2440198888
Brief (Lamb et al. 2024b)
(DOI:
10. 10.00//41558-0240119923
Nature Climate Change; Accessed: 2025-05-10.
44. Paper (Gasparini et al. 2024b) (DOI: 10. (038//41558-004-019777
Brief (Gasparini al.
2024a) 10000 155-044491180024-9999--77 Nature Climate Change; Accessed: 2025-05-100 10.
45. Paper (Duan et al. 2024a) (DOI: (1111///415588-02--1192222 Brief (Duan et al. 2024b) (DOI:
10. 10111//41558-022-01110000
Nature Climate Change; Accessed: 2023-05-10.
46. Paper (Andreoni et al., 2024b) (DOI: 10.10088411588-02--1880000
Brief (Andreoni et al.
2024a) (DO1:
Nature Climate Change; Accessed: 100
47. Paper (Linsenmeier et al. 2023a) (DOI: 10. (033///4158-002-001111111
Brief (Linsenmeier et al.
2023b) (DOI: 10.1038/ 155-:-0331155--000000000000
Nature Climate Change; Accessed: 2025-05 10.
48. Paper (Merfort et al. 2023a) (DOI: 112.10/8//41558-023-0111
Brief (Merfort et al. 2023b)
(DOI: 0011088/441558-023-0117
Nature Climate Change; Accessed: 2025-05- 10.
49. Paper (Cerf et al. 2023a) (DOI: 10. 11103894115580023-06679999 Brief (Cerf et al.
2023b
(DOI:
10. 100000/413588-02--111
Nature Climate Change; Accessed: 2025-05-100 10.
50. Paper (Buck et al. 2023b) (DOI: (0.10388/411558-02200115
Brief (Buck et al. 2023a
(DOI:
10. 10.1038/<41155-02-01111.1
Nature Climate Change; Accessed: 2025-05-10.
51. Paper (Harring et al. 2023a) (DOI: 10. 0000l41158-023-0100000
Brief (Harring et al.
2023b)
(DOI: 00.1088//411588003--01100
Nature Climate Change; Accessed: 2025-05-10.
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52. Paper (Basheer et al. 2023a) (DOI: (0.1038//41155-002220002
Brief (Basheer et al.
2023b
(DOI: 001003//4415500222001555555
Nature Climate Change; Accessed: 2025-05 10.
53. Paper (yer et al. 2022b) (DOI: 07728/441558-022-055000 Brief (Iyer al. 2022a
(DOI:
10. 1000///1158-0022000000 177))) Nature Climate Change; Accessed: 2025-05-10.
54. Paper (de Ruig al. 2022b) (DOI: 0010088/41155--02--01155
Brief (de Ruig et al.
2022a)
55. Paper (Bjöm al. 2022a) (DOI: Nature Climate Change; Accessed: 2025-05-
(DOI: (10138/41155-002--0150000
10.
Brief (Bjomn et al. 2022b (DOI:
01158//41358-022-013333
Nature Climate Change; Accessed: 2025-05-10.
56. Paper (Mildenberger al. (022a) (DOI: 10. 01018/444158-021-122251 Brief (Mildenberger
(et al. 2022b) (DOI: 10.1033/441388-001-00000000
Nature Climate Change; Accessed: 2025-05-
10.
57. Paper (Budolfson et al. a021a) (DOI: 10. 1580008011111000-11111111111
Brief (Budolfson et al.
2021b) (DOI: 155110////455550200-0122-------0
Nature Climate Change; Accessed: 2025-05- 10.
58. Paper Janssens et al. 2020) (DOI: (1588/41558-020-004444 Brief (Janssens ct al. 2021
(DOI:
01088//411558--21112000 Nature Climate Change; Accessed: 2025-05-
59. Paper (Moffette et al. 2021b) (DOI: (001////1558-00000095666666 Brief (Moffette al.
2021a)
(DOI: 00.1038/4115::0010011155
Nature Climate Change; Accessed: 2025-05- 10.
60. Paper (Bechtel et al., 2020) (DOI: 10. 10008//4155800000000000010
Brief (Bechtel et al.
2021
(DOI: 001008/441558--211011032
Nature Climate Change: Accessed: 2025-05- 10.
61. Paper (Peng et al. 2021b) (DOI: (00.0338/41155--02--002233
Brief (Peng et al. 2021a
(DOI:
10. 10.1038/s41558-00 02--588802--113-555 Nature Climate Change; Accessed: 2025-05-10.
62. Paper (Tran et al. 2024) (DOI: 10. (0.10338/44224-024-000 16-7); Brief ([vanov et al.
2024
(DOI:
10. 110088//428440220000000
Nature Cities; Accessed: 2025-05-10.
63. Paper [Diezmartinez et al. 2024b) (DOI: 10. (033///4224-00440002221--11
Brief Diezmartínez
et al. 2024a) (DOI: 1033//442440024-0022223
Nature Cities; Accessed: 2025-05-10. 10.
64. Paper (Mollbom et al. 2025a (DOI: 10.1177/0022 466044224462242559449 1017000111077721111 Brief (Mollborn et al.
2025b) (DOI: 10.11777002146555111113
Journal Of Health And Social Behavior; Accessed:
2025-05-10.
65. Paper (Moinester and Stanhope 2024a) (DOI: (000:000.17700222444133333 Brief (Moinester
and Stanhope| 2024b) (DOI: TO. 0..1177000214652422289111 Journal Of Health And Social
Behavior; Accessed: 2025-05-10.
66. Paper (Han et al. 2024a) (DOI: 101117/00211452312226666 Brief (Han et al.
2024b) (DOI:
111177002216652411
Journal Of Health And Social Behavior; Accessed: 2025-05-10 10.
67. Paper (Dore et al. 2024a) (DOI: 10.1177/0022 142221166631111 194043); Brief (Dore et al.
20246) (DOI:
10. 10.117770222145544111110 12266228 Journal Of Health And Social Behavior, Accessed: 2025-05-10 10.
68. Paper [McFarland et al. (203a)) (DOI: Journal Of Health And Social Behavior; Accessed:
109202); Brief (McFarland et al.
(2023b) (DOI: 10.17770022444222
2025-05-10.
69. Paper (Masters et al. 2023a
(DOI: 10.17770002146553111633
Brief (aasters et al. 2023b)
(DOI: 10.1177/0022 46523
171627); Journal Of Health And Social Behavior; Accessed: 2025-
05-10.
70. Paper (Parbst and Wheaton
2023a) (DOI: 10. 10.1177/0022244652311 166334); Brief (Parbst and
Wheaton 2023b) (DOI:
190977; Of Health And Social Behavior;
Accessed: 2025-05-10,
71. Paper (Czarnecki et al. 2023a (DOI: 101000000017770211533111117777 Brief (Czarnecki et al.
20236b (DOI: 10.11770022
Journal Of Health And Social Behavior; Accessed:
2025-05-10.
72. Paper (McCabe 2022a) (DOI: 10.1177/002246552 1058152)) 152); Brief (McCabe 2022b) (DOI:
211177000221655222 1097453)) Journal Of Health And Social Behavior; Accessed: -000
73. Paper (Vuolo et al. 2022a) (DOI: 011777002221455110077777
Brief (Vuolo et al.] 20226) (DOI:
0000214652211111 01.17770002115522111
Journal Of Health And Social Behavior; Accessed: 2025-05-10.
74. Paper (Anderson and Ray- Warren (022a) (DOI: 10.1777002214552217799555) Brief (Anderson)
Ray-Rarraarr 2022b) (DOI: 10. 10.1177/0021166533 030555222211191111 130017); Journal Of Health And Social
Behavior; Accessed: 2025-05-10.
75. Paper (Augustine) 202 (DOI: 10. 10.11770022
Brief (Augustine) 2021b) (DOI:
10.11770022146513 20986008; Journal Of Health And Social Behavior; Accessed: 2025-05-10 10.
76. Paper (Manzer and Bell 2021a) (DOI: 10.1177/0022 011770002214652111 1003222)::: Brief (Manzer and Bell
2021b) (DOI: 10.11770002246522 11177700021455211008888888 1008328); Journal Of Health And Social Behavior, Accessed:
2025-05-10.
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77. Paper (Berg al. a0 (DOI: 10.117700221465511022220 Brief (Berg et al. 2021b) (DOI:
0111770022116521100555
Journal Of Health And Social Behavior; Accessed: 2025-03 000-005-11 10.
78. Paper (Schnittker and Do 2020a) (DOI: 10. 10.1177/0022146511999 165119991111)) Brief (Schnittker and Do
2020b) (DOI: 10117770222444550000000 Joura Of Health And Social Behavior; Accessed:
2025-05-10.
79. Paper (Owens) 2020a (DOI: 10.1177/002146222944480000 Brief (Owens) 2020b) (DOI:
10.1177/0022 011000022145202261111 Journal Of Health And Social Behavior; Accessed: 2025-05-
10.
80. Paper (Thombs et al. 2020a) 1.1100021.1177022244999911111119 Brief (Thombs et al.) 2020b)
(DOI: T0. 4652044467);;
Of Health And Social Behavior; Accessed: 2025-
05-10.
81. Paper (Bierman and Schieman 2020a) (DOI: 10. 10111177777002214452770000000000 Brief (Bierman and
Schieman 2020bb (DOI: 10.1 1100::00.1777002222666677770,,00
Of Health And Social Behavior;
Accessed: 2025-05-10.
82. Paper (Zhang et al. 2025b (DOI: 10.10088/441893-255-1153333 Brief (Zhagg et al. 2025a
(DOI: 0.1008//4183--055-01553
Nature Sustainabilityy; Accessed: 2025-05-10.
83. Paper (Azar) 2024a)
111770002214552412221 (DOI: 10. 10.17770021465541166445555 Brief (Azar 2024b) (DOI:
Journal Of Health And Social Behavior; Accessed: 2025-05-10.
84. Paper (Rapp et al. (2022a (DOI: 10.1177/0022 153); Brief (Rapp et al.
20226) (DOI:
Journal Of Health And Social Behavior; Accessed: 2025-05-10.
85. Paper (Ci et al. 20245) (DOI: (0.1038441893:004444471711 Brief (Ci et al. 2024a) (DOI:
10. 0108//44893-024-01444
Nature Sustainability; Accessed: 2025-05-10.
J.2 PUBLICATION YEAR DISTRIBUTION OF 85 POLICY BRIEFS
As shown in Table 44 the number of policy briefs grows steadily. This trend highlights the increasing availability of high-quality published data. This suggests strong potential for expanding Sci2Pol- Bench and Sci2Pol-Corpus in the future.
Table 44: Publication Year Distribution of the 85 Expert-written Policy Briefs.
Year
2019
2020
2021
2022
2023
2024
2025
Num. of Pairs
6
15
10
11
12
20
11
J.3.
LIST OF NEW EXPERT-WRITTER PAPER-BRIEF PAIRS
We document the new 8 expert-written scientific paper-policy brief pairs for Task 15.
Paper
(Wbeer et al. 2025b) (DOI: (0.1008/441899--255066666-66-- Brief (Weber al.
2025a
(DOI:
020-00000006622): Nature Sustainability; Accessed: 2025-05-10. 10.
Paper Minten et al., 20256)
(DOI: 10. 1033:4418999005500111--))) Brief (Minten et
2025a)
(DOI: 19775/544183---05-111116 Nature Sustainabiliity; Accessed: 2025-05-10.
Paper (Vivier et al., 2025b) (DOI: 10. 1033/44155--025-024444)) Brief (Vivier et al.
2025a)
(DOI: 00.1088/41155--055--00
Nature Climate Change: Accessed: 2025-05-10 10.
4. Paper (Cai et al. 2025) (DOI: 10. (00SS344119--025-0555500)) Brief (Wang et al. 2025a)
(DOI:
10. 00385541330005-01155
Nature Sustainabilityy Accessed. 2025-05-10. 10.
5. Paper (Hermansen et al. 2025a) (DOI: 10. 00010088/411586-05500055555
Brief (Hermansen et al.
2025b) (DOI: 000008000001800000-20220
Nature; Accessed: 2025-05-10.
6. Paper (Sun et al. 2025b) (DOI: (0.1103//44150 160-100-00-022--1111>>> Brief (Sun et al. 2025a (DOI:
10. 1008/44150-005501555
Nature Energy: Accessed: 2025-05-10.
7. Paper (Anshsssi and Townsend (025b) (DOI: 100.1000//41183-25-000066
Brief Anshassi Nature Sustainability; Accessed:
and Townsend 2025a) (DOI: 10. 2025-05-10.
Paper (Dipoppa and Gulzar 2024) (DOI: 10. 000000884411586-02400006666 Brief (Dipoppa and Gulzar 2025) (DOI:
Nature; Accessed: 2025-05-10.
J.4 LIST OF 3 NEW IN IN-CONTEXT EXPERT-WRITTEN PAPER-BRIEF PAIRS
We document the new 3 expert-written scientific paper-policy brief pairs for in-context polishing.
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1. Paper (Mehta et al. 2025a) (DOI: 10. 10.0030/41893-025-115444))) Brief Meehta al.
2025b)
(DOI: 10.1038/441893-00 00-00005990)002-))99999 Nature Sustainability; Accessed: 2025-05 10.
2. Paper (Wang al. 2025c) (DOI: (0038//41558-0025-244999999 Brief (Wang et al.
2025b
(DOI:
10. 15808885/11158-5552224 8-77; Nature Climate Change; Accessed: 2025-05-10.
3. Paper (Ye al. 2025a) (DOI: 10.
Brief (Ye al.
2025b)
(DOI:
10. 10.0038844115000055111000-00- Nature Energy; Accessed: 2025-05-10.
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K EXAMPLES FOR TASKS 1-18
In this section, we provide examples for Tasks 1-18, as show Tables 45 to 62
Table 45: Example for Task Scientific Text Autocompletion.
Prompt:
You are given the start of a paragraph from a scientific research paper, ending in "...". Choose the most coherent continuation from the options below.
Query:
We do not find an association between exposure to climate hazards and re-risking, Instead, we find that de-risking positively and significantly correlated with higher exposure physical climate risks...
Options:
A. This is puzzling and requires further analysis. B. Similarly, a large financial sector increases the direct exposure of economy to stranded asset risks, which could threaten financial stability.
C. It follows that if a country is highly exposed to physical climate risks, it would adopt these practices.
D. In fact, prior research has demonstrated a correlation between physical risks and central bank management of climate risks.
E. We measure climate hazard exposure by using the exposure component of the Notre Dame Global Adaptation Initiative (ND-Gain) country index.
Answer with the letter (A-E) corresponding to the best continuation. Strictly follow this format- do not include any explanations or additional text.
Answer:
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Table 46: Example for Task 2: Political Text Autocompletion.
Prompt:
You are given the start of a paragraph from a policy brief, ending in "...". Choose the most coherent continuation from the options below.
Query:
The rapid diffusion of battery electric vehicles, in addition to the decarbonization of the energy sector-requires an increasing number of batteries. However, the EU's goal to cover 90% of its battery demand from domestic production by 2030 is risk, projected demand will likely exceed 1.0 TWh per year and outpace production capacity despite highly ambitious growth rates.
Options:
A. If Europe fails to scale up production, it may face severe economic and geopolitical risks, due increased dependence external suppliers, weakened industrial competitiveness and potential for delayed decarbonization.
B. An urgent question is therefore whether Europe can realistically meet its future battery demand through domestic production, and what policy actions are needed to ensure success.
C. We find that European battery cell demand will likely surpass 1.0 TWh per year by 2030, whereas domestic production capacity is expected to fall short, creating risk of supply con- straints.
D. Although Europe can be expected to meet at least 50-60% of its demand through domestic production by 2030, achieving the EU's 90% sufficfffic target feasible but uncertain, as nearly half of our modelled scenarios fail to meet this target (Fig. 1).
F. If Europe wants more independence from battery cell imports, our findings highlight the urgency of accelerating production capacity expansion, scaling up a battery supply chain, and implementing strong industrial policies to support competitiveness and supply sovereignty.
Answer with the letter (A-E) corresponding to the best continuation. Strietly follow this format- do not include any explanations or additional text.
Answer:
Table 47: Example for Task Scientific Sentence Reordering.
Prompt:
You are given three shuffled sentences that originally formed coherent paragraph from scientific research paper. Your task to determine the correct order by selecting the most logical and coherent sequence.
Shuffled Sentences:
A. Other key markets, such as the United States and China, have also set ambitious ZEV targets from the 2030s.
B. Accordingly, BEVs prevail in the future portfolios of car manufacturers and several European countries will enforce 100% zero-emission vehicle (ZEV) sales for cars by at least 2035, banning large-scale sales of conventional vehicles as sufficient quantities of sustainable fuels are unlikely. C. While some studies have emphasized the difficulties involved in decarbonizing transport, there is robust evidence that battery electric vehicles (BEVs) will form the backbone of future low-carbon road transport.
Answer with a permutation of A. B, and that best restores the original paragraph (c.g., BAC, CAB). Strietly follow this format-do not include any explanations additional text.
Answer: CBA
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Table 48: Example for Task 4: Political Sentence Reordering.
Prompt:
You are given three shuffled sentences that originally formed coherent paragraph from a policy brief document. Your task is to determine the correct order by selecting the most logical and coherent sequence.
Shuffled Sentences:
A. Our approach accounts for uncertain ties such as construction delays, utilization rates, and evolving market conditions, and assesses corresponding raw material needs.
B. Our study uses probabilistic modelling to project future battery demand and domestic production in Europe and evaluates Europe's pathway towards battery self-sufficiency via probabilistic statements.
C. We independently model demand and supply using S-shaped diffusion curves based on historical data, actual announced production capacities, and practice-oriented findings about how these announced capacities materialize over time.
Answer with a permutation of A. B, and that best restores the original paragraph (c.g.. BAC, CAB). Strictly follow this format-do not include any explanations additional text.
Answer: CAB
Table 49: Example for Task 5: Sentence Classification.
Prompt:
You given sentence or paragraph from scientific research paper. Classify the text into of the following five categories relevant to policy briefs:
1. Policy Problem Describes the societal or strategic issue that the research aims to address. Scientific Research Findings Reports key empirical results, model outcomes, or discoveries from the study.
3. Scientific Research Study Methods Details the data sources, methodologies, models, or analytical procedures used.
4. Policy Implications Explains how the research findings can inform, influence, or support policy decisions.
5. None Does not into any of the above categories.
Your response must be exactly one of the following: Policy Problem Scientific Research Findings Scientific Research Study Methods Policy Implications None
Text:
Batteries are critical mitigate global warming, with battery electric vehicles as the backbone of low-carbon transport and the main driver of advances and demand for battery technology. However, the future demand and production of batteries remain uncertain, while the ambition to strengthen national capabilities and self-sufficiency is gaining momentum. In this study, leveraging probabilistic modelling, we assessed Europe's capability to meet its future demand for high-energy batteries via domestic cell production. We found that demand in Europe is likely to exceed 1.0 TWh yr- 1 by 2030 and thereby outpace domestic production, with production required grow at highly ambitious growth rates of 31-68% yr-1. European production is very likely to cover at least 50-60% of the domestic demand by 2030, while 90% self-sufficiency seems feasible but far from certain. Thus, domestic production shortfalls are more likely than not.
Category:
Answer: Policy Problem
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Table 50: Example for Task 6: Scientific Knowledge Understanding
Prompt:
You are given multiple-choice question. Read the question carefully and select the best answer from the provided options. Each option is labeled with capital letter (A, B, C... Only one answer is correct. Provide your response as a single letter (e.g., A, B, C, ...).
Question:
An ileostomy opening should be:
Options:
A. less than lcm in length.
B. invisible the naked eye.
C. 3-5em in length.
D. below skin level.
E. protruding 10cm from the skin.
over 10cm length.
G. over 5cm in length.
Answer with single letter (c.g.. A. B, .... Strictly follow this format-do not include any explanations or additional text.
Answer: C
Table 51: Example for Task 7: Policy Problem Summarization.
Prompt:
You are given passage from a scientific paper that describes part of the policy problem motivating the research.
Summarize the specific issue mentioned in the passage using policy-brief style sentences. Your output should:
Highlight the problem challenge described
Use accessible, non-tecion non-technical language (technical terms are allowed when necessary)
Focus only on what present in the passage
Scientific Text:
Research shows that restrictive immigration policies and practices are associated with poor health, but far less known about the relationship between inclusive immigration policies and health. Using data from the United States natality files, we estimate associations between state laws granting undocumented immigrants access to driver's licenses and perinatal outcomes among 2,047,067 singleton births to Mexican and Central American immigrant birthing people (2008- 2021). Fitting multivariable log binomial and linear models, we find that the implementation of license law is associated with improvements in low birthweight and mean birthweight. Replicating these analyses among U.S.-born non-Hispanic White birthing people, we find no association between the implementation of license law and birthweight. These findings support the hypothesis that states' extension of legal rights to immigrants improves the health of the next generation.
Summary:
Answer:
None (Use Gemini-2.5-Pi as the judge).
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Table 52: Example for Task 8: Research Findings Summarization.
Prompt:
You are given a passage from a scientific paper that describes part the research findings.
Summarize the specifie findings using policy-brite brief style sentences. Your output should:
Clearly state the result(s) presented in the passage
Use plain and direct language (technical terms are allowed when necessary) Focus only on what is present in the passage
Scientific Text:
Table displays the results of the ordered logistic regression analyses of community distrust and subjective social isolation. Model shows that, independent of the controls, respondents in 2020 evidenced significantly increased risk of community distrust. Being respondent in the 2020 survey was associated with almost 50% greater odds of reporting a higher level of distrust than being respondent in the 2019 sample. However, these between-way differences did not vary by age; the interaction between wave of survey and age in Model 2 is not significant.
Summary:
Answer: None (Use Gemini-2.55 2ro-Po as the judge).
Table 53: Example for Task 9: Study Methods Summarization.
Prompt:
You are given a passage from a scientific paper that describes part of the research study's methodology.
Summarize the method approach using policy-briet style sentences. Your output should:
Describe the model, data, procedure mentioned in the passage
Use clear and accessible language (technical terms are allowed when necessary)
Focus only on what is present in the passage
Scientific Text:
The survey data were collected by the Energy Institute at the Johannes Kepler University Linz, following high European Union standards of data protection and voluntary study participation. The methodology used in this paper does not require institutional ethical approval according to the guidelines set out by the Energy Institute the Johannes Kepler University Linz. Confiden- tiality and anonymity of participants were ensured, and informed written consent was obtained from all the interviewees.
Summary:
Answer:
None (Use Gemini-2.5. P.5-Pro as the judge).
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Table 54: Example for Task 10: Policy Implications Summarization.
Prompt:
You are given a passage from a scientific paper that describes part of the policy implications of the research findings.
Summarize the implications using volicy-briet style sentences. Your output should:
Explain how the described result or observation could inform or influence policy
Use accessible language (technical terms are allowed when necessary)
Focus only on what is present in the passage
Scientific Text:
This study conducted in Chicago, Illinois. Although these data collected before recreational marijuana use was legalized in Illinois, is possible that attitudes toward marijuana use are generally more permissive Chicago relative to other parts of the country. Future research should examine regional variations in responses to substance use in perinatal care settings. In addition, this analysis is premised on the uptake of legal tasks by individual providers in settings where testing is based on provider discretion. Studies examining how law shapes provider-pation interactions in systems where universal drug testing is carried out may reveal different patterns and outcomes. Another limitation of this study is that the data reflect providers' interpretations and perceptions of hospital testing protocol. In the future, researchers should examine official, documented hospital protocols understand how formal organizational policy shapes provider practices. Finally, given sample size limitations and the wide range of state and organizational responses to substance not intended to capture practices in perinatal care by and large. Rather, these findings reflect dilemmas workers may contend with in settings where they given latitude over the degree to which they adopt legal tasks or not.
during pregnancy, these findings are
Summary:
Answer: None (Use Gemini-2.5-Pro the judge).
Table 55: Example for Task 11: Policy Problem Generation.
Prompt:
You are given the full text of a scientific research paper.
Identify and summarize the policy problem that this research addresses. Your response should:
Clearly state the societal, environmental, or strategic issue motivating the study
Be written in accessible, policy-brief style sentences
Focus on the core challenge the research aims solve
Avoid excessive detail or technical jargon unless necessary for clarity
Write short paragraph suitable for inclusion under the "Policy Problem" section of policy brief.
Scientific Research Paper: [Scientific paper text)
Policy Problem:
Answer: The rapid diffusion of battery electric vehicles - addition to the decarbonization of the energy sector requires increasing number of batteries. However, the EU's goal to cover 90% of its battery demand from domestic production by 2030 is at risk, as projected demand will likely exceed 1.0 TWh per year and outpace production capacity despite highly ambitious growth rates. If Europe fails to scale up production, may face severe economic and geopolitical risks, due increased dependence on external suppliers, weakened industrial competitiveness and potential for delayed decarbonization. An urgent question is therefore whether Europe can realistically meet its future battery demand through domestic production, and what policy actions are needed to ensure success.
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Table 56: Example for Task 12: Research Findings Generation.
Prompt:
You are given the full text of scientific research paper.
Summarize the paper's key research findings in the style of policy brief. Your response should:
Focus on the major empirical model-based results
Present findings that are policy-relevant and supported by the study
Use concise, non-technical language with essential technical terms as needed
Avoid quoting directly or summarizing minor details
Write short, clear paragraph appropriate for the "Scientific Research Findings" section of policy brief.
Scientific Research Paper: (Scientific paper text)
Scientific Research Findings:
Answer: We find that European battery cell demand will likely surpass 1.0 TWh per year by 2030, whereas domestic production capacity is expected to fall short, creating a risk of supply constraints. Although Europe can be expected to meet at least 50-60% of its demand through domestic production by 2030, achieving the EU's 90% self-sufficience target is feasible but uncertain, as nearly half of our modelled scenarios fail to meet this target. If Europe wants more independence from battery cell imports, findings highlight the urgency of accelerating production capacity expansion, scaling up battery supply chain, and implementing strong industrial policies support competitiveness and supply sovereignty. Our approach broadly applicable to regions aiming for battery self-sufficiency and should be examined with interacting factors such as policy support and supply chain resilience. However, our analysis does not account for disruptive market shifts, policy reversals, or unexpected technological breakthroughs, which could substantially alter production and demand trajectories.
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Table 57: Example for Task 13: Study Methods Generation.
Prompt:
You are given the full text of scientific research paper.
Summarize the research methods used in the study in way that is informative for policy audiences. Your response should:
-eeccribe the overall study design, data sources, tools, and models used
Avoid unnecessary technical details or equations
Use accessible but precise language
Write short paragraph suitable for the "Scientific Research Study Methods" section of a policy brief.
Scientific Research Paper: (Scientific paper text)
Scientific Research Study Methods:
Answer: Our study uses probabilistic modelling to project future battery demand and domestic production Europe and evaluates Europe's pathway towards battery self-sufficiency via probabilistic statements. We independently model demand and supply using S-shaped diffusion curves based on historical data, actual announced production capacities, and practice-oriented findings about how these announced capacities materialize over time. Our approach accounts for uncertainties such as construction delays, utilization rates and evolving market conditions, and material needs. This study is particularly relevant given Europe's policy push toward climate neutrality and striving for resilient, sustainable battery value chains with domestic production and global competitiveness. By applying an established technology diffusion framework, we provide a robust, scenario-based outlook rather than relying on overly optimistic industry projections. Our method suitable for evaluating long-ter term-termm industrial transformation and supply chain resilience, making it applicable to other regions with similar ambitions.
assesses corresponding
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Table 58: Example for Task 14: Policy Implications Generation.
Prompt:
You given the full text of scientific research paper.
Summarize the study's s policy implications how the research findings can inform policy or decision-making Your response should:
Interpret what the results suggest for governments, regulators, institutions
Highlight potential actions, strategies, or decisions informed by the study
Use policy-approoriate language without speculation beyond the study's conclusions
Be clear, practical, and informative Write short paragraph for the "Policy Implications" section of a policy brief.
Scientific Research Paper: (Scientific paper text)
Policy Implications:
Answer:
Focus on net materialized production capacities rather than mere corporate announcements to ensure realistic policy planning and avoid overestimations, while ensuring minimal level of production by local companies utilizing domestic intellectual property.
Create predictable and reliable framework conditions for industry and end users stimulate market demand and allow capacity announcements to materialize.
Strengthen public-private partnerships to de-risk investments and streamline European regulations to accelerate the scale-up of battery production and regional supply chains.
Create competitive differentiation and level playing field via de-risking industrial policies, sustainability criteria, and local content requirements at the European level, and carefully balance trade policies to foster competitiveness with options for global collaboration and learning.
Ensure continuous policy support and cascade research and development policies with industrial policies in terms of timing and scope in accordance with the scale-up of battery production and an evolving battery value chain.
Table 59: Example for Task 15: Policy Brief Generation.
Prompt: You are given the full text of scientific research paper.
Your task is to generate structured policy brief that includes the following components:
A concise, descriptive title that captures the central theme or policy issue addressed in the paper (do not label it with "Title:").
Policy Problem What societal or strategic challenge is the paper addressing?
Scientific Research Findings What the key results relevant to this issue?
Scientific Research Study Methods What methodology or data supports these findings?
Policy Implications How might the findings guide or influence policy?
Write each section clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.
Scientific Research Paper: (Scientific paper text)
Policy Brief:
Answer: [Full Policy brief text)
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Table 60: Example for Task 16: Scientific Claims Verification.
Prompt:
You are given a passage from a scientific paper and a research finding derived from it.
Your task to determine whether the finding is fully supported by the information provided in the passage.
Respond with word:
SUPPORT- - if the finding is clearly and accurately justified by the passage CONTRADICT if the finding misrepresents, exaggerates, is not derivable from the passage
Do make assumptions beyond the provided text. Use only the given evidence.
Scientific Text:
Fuelled by substantial BEV diffusion up 2035, European battery demand is likely to surpass 1.0 TWhyr-1 by 2030 (in 69% all scenarios). The interquartile range (IQR) in 2030 is 0.97-1.2 TWh yr-1. Some high-demand scenarios may exceed the TWh threshold early as 2026 and even approach 1.6 TWh yr- by 2030, with the top 10% exceeding 1.30 TWh yr-1.
Claimed Finding: In around 69% of model runs, European battery demand exceeds TWhh per year by 2030.
Answer with SUPPORT or CONTRADICT.
Strictly follow this format-do not include any explanations or additional text.
Answer (SUPPORT/ CONTRADICT)
Answer: SUPPORT
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Table 61: Example for Task 17: Scientific Claims Verification 2.
Prompt:
You will be shown a claim related the COVID-19 pandemic and the abstract of biomedical research paper. Each sentence from the abstract will be on a separate line, Your task is to return JSON object with two fields:
"verdict": The fact-checking verdict. If the information in the abstract supports the claim, write "SUPPORT". If the abstract contradicts the claim, write "CONTRADICT". ""evidene":: An array of sentences providing evidence for the verdict. Please copy all relevant sentences verbatim from the abstract.
For instance, if the model were given the claim "the COVID vaccine is safe for healthy adults", the output might be
"verdict": "SUPPORT", "evidence": ["Our findings indicate that the risks of severe COVID vaccine side effects are low for healthy adults."]
The d614g mutation the sars-cov2 spike protein reduces infectivity in an ace2 receptor- dependent manner
Claim:
Abstract:
The SARS-CoV2 coronavirus responsible for the current COVID19 pandemic has been reported to have relatively low mutation rate.
Nevertheless, a few prevalent variants have arisen that give the appearance of undergoing positive selection they becoming increasingly widespread over time. Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.
The D614G substitution, however, is in linkage disequilibrium with the ORFIb P314L mutation, where both mutations almost invariably co-occur, making functional inferences problematic.
In addition, the possibility of repeated new introductions of the mutant strain does not allow one to distinguish between a founder effect and intrinsic genetic property the virus.
Here, we synthesized and expressed the WT and D614G variant SARS-Cov2 Spike protein, and report that using a SARS-CoV2 Spike protein pseudotyped lentiviral vector we observe that the D614G variant Spike has > 1/2 log(10) increased infectivity in human cells expressing the human ACE2 protein as the viral receptor. The increased binding/fusion activity of the D614G Spike protein corroborated in cell fusion assay using Spike and ACE2 proteins expressed in different cells. These results consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-COV2.
Answer:
"verdict": "CONTRADICT".
"evidence": [
"Most prominent among these is the D614G amino acid substitution in the SARS-CoV2 Spike protein, which mediates viral entry.",
"The increased binding/fusion activity of the D614G Spike protein was corroborated in cell fusion assay using Spike and ACE2 proteins expressed in different cells.", "These results are consistent with the possibility that the Spike D614G mutant increases the infectivity of SARS-CoV2."
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Table 62: Example for Task 18: Policy Implications Verification.
Prompt: You are given the full text of scientific research paper and proposed policy implication.
Your task is to determine whether the policy implication is fully supported by the scientific research paper.
Respond with one word:
SUPPORT if the implication clearly follows from the paper's results and interpretations
CONTRADICT if the implication misrepresents, overstates, or is not justified by the evidence or conclusions
Base your judgment only what the paper states. Do not speculate beyond the provided content.
Full Scientific Paper: (Scientific paper text)
Policy Implication:
If Europe wants more independence from battery cell imports, our findings highlight the urgency of accelerating production capacity expansion, scaling up a battery supply chain, and implementing strong industrial policies to support competitiveness and supply sovereignty.
Answer with SUPPORT or CONTRADICT.
Strietly follow this format-de not include any explanations or additional text.
Answer (SUPPORT/ CONTRADICT:
Answer: SUPPORT
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L PROMPTS FOR SCI2POL-BENCH EVALUATION AND DATASET CURATION
We provide detailed prompts used for LLM evaluation and dataset curation for Sci2Pol-Bor BlBhhhhh.
L.1 TASKS 7-10 PROMPT FOR REFERENCE-FREE SCORE
We present the detailed prompt used for Gemini-2.5-Pro when serving as the evaluation judge for Tasks 7-10 in Table 63 We average the scores from the JSON output and multiply the result by 20 to scale to a 0-100 range.
Table 63: Prompt for LLM-based Judge for Summarization Tasks.
Prompt:
You are strict and critical evaluator of summaries. Evaluate the summary on the following dimensions using 1-5 scale (1 very poor, 5 = excellent). Be conservative in your judgments: do not give high scores unless the summary genuinely outstanding.
(1) Clarity: whether the summary reader-friendly and expresses ideas clearly.
(2) Accuracy: whether the summary contains the same information as the source document.
(3) Coverage: how well the summary covers the important information from the source docu- ment.
(4) Overall quality: how good the summary is overall at representing the source document;
good summary is shorter piece of text that has the essence of the original and tries to convey the same information as the source document.
Return only JSON object in this format:
"clarity": <1-5>,
"accuracy": <1-5>,
coverage" <1-5>,
Leverall_quaittee <1-5>
Source Passage:
(source passage t
Summary: (summary text)
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L.2 TASK 11 PROMPT FOR REFERENCE BASED SCORE
We present the detailed prompt used for Gemini-2.5-PP when serving as the evaluation judge for Tasks 11 (Policy Problem Generation) in Table 64 and Table 65
Let prob_imp and prob_qual be their respective JSON outputs, and define the component set
C (background, existing_problemt consequence, attention_problett suppotting_detail)
We compute the score of Task 11
Sraw
Σ
prob_ prob_imp[c] prob_quall
GEC
We then multiply the raw score by 20 to scale it to maximum value of 100.
Table 64: Prompt for LLM--hased Judge for Task 11: Policy Problem Generation (Importance).
Prompt:
You are strict policy-brief evaluator. Given the full scientific PAPER, assign an importance score to each structural component for effectively communicating the policy problem, based only on the PAPER.
Components:
(1) background what drives the problem (e.g., scientific, environmental, economic context).
(2) existing_problem the current obstacle, mismatch, challenge.
(3) consequence - potential risks if the problem is not addressed.
attention problem attention_problemm the key policy issue or question requiring urgent attention.
(5) supporting detail - clarification or elaboration of any of the above.
Scoring Instructions:
Assign an importance score between 0.0 and 1.0 for each component.
A higher score means the component is essential for understanding the policy problem described in the PAPER.
A lower score
the component is optional, minor, not clearly relevant. a component is not justified by the PAPER, assign 0.0.
Strict-grading Instructions:
Score conservatively: if unsure, choose the lower score.
Base each score only on the PAPER-no external references or assumptions.
Return exactly the JSON object below (no explanations, no extra keys).
Return only JSON object in this format:
"background": <0.0-1.0>, <0.0-1102
"existing_problent" <0.0-1.0x,
"consequence". <0.0-1.02. <0.0-1.0>, "attentioo__robleett <0.0-1.0>,
"supporting_detaii".. <0.0-1.0>>>>>
PAPER: (full PAPER text)
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Table 65: Prompt for LLM-based Judge for Task 11: Policy Problem Generation (Quality).
Prompt:
You are a strict policy-brief evaluator. Given the full scientific PAPER and the CANDIDATE'S policy problem paragraph, assign quality scores to five aspects of how well the problems are conveyed in CANDIDATE POLICY PROBLEM.
DOLICY_PROBBEM.
Components:
(1) background what drives the problem (e.g., scientific, environmental, economic
context).
existing prriiigmppooemmmmeee the current obstacle, mismatch, or challenge.
(3) consequence potential risks if the problem is not addressed.
attention
Drcentionppobblm-e problem the key policy issue or question requiring urgent attention.
(5) supporting_detant - clarification or elaboration of any of the above.
Scoring Instructions:
Assign a quality score between 0.0 and 1.0 for each component.
A higher score means the content is clear, logical, and strongly aligned with the PAPER.
A lower score means the content vague, incorrect, poorly structured, or missing.
If component is not addressed, assign 0.0.
Strict-grading Instructions:
Score conservatively: if unsure, choose the lower score.
Base each score only on comparisons between PAPER and CANDIDATE.
Return exactly the JSON object below (no explanations, no extra keys),
Only evaluate content
Return only a JSON object in this format:
"backgroundd": <0.0-1.0>,
"existingg_probeen"" <0.0-1.0>,
"consequence": <0.0-10>>,
"attentiion_prbleett" <0.0-1.0>, "supporting__eeaii"" <0.0-1.0>
PAPER: [full PAPER text)
CANDIDATE POLICY PROBLEM:
[candidate policy problem paragraph)
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L.3 TASK 12 PROMPT FOR REFERENCE BASED SCORE
We present the detailed prompt used for Gemini-2.5-PPe when serving as the evaluation judge for Task 12 (Research Findings Generation) in Table 66 We average the scores from the JSON output and multiply the result by 100 to scale to a 0-100 range.
Table 66: Prompt for LLM-based Judge for Task 12: Research Findings Generation.
Prompt:
You strict policy-brief evaluator. Given the full scientific PAPER and the CANDIDATE'S findings section, assign quality scores to five aspects of how well the findings are conveyed in CANDIDATE FINDINGS.
Criteria:
(1) completeness does the section include all important findings from the PAPER?
(2) importance are the findings mentioned actually important according to the PAPER?
(3) accuracy are the described findings factually correct and consistent with the PAPER?
(4) summarizing findings does the section effectively emphasize and summarize the key
messages or implications from the data, rather than just listing facts?
(5) specification to fo_ddinggs does the section clarify the scope, context, or limitations of the findings, including conditions under which they apply?
Scoring Instructions:
Assign a score between 0.0 and 1.0 for each criterion.
higher score means the section performs well on that criterion.
A lower score means the section is vague, misleading, incomplete, missing that dimension.
Strict-grading Instructions:
Score conservatively: if unsure, choose the lower score.
Base each score only on comparisons between PAPER and CANDIDATE.
Return exactly the JSON object below (no explanations, no extra keys).
Return only a JSON object in this format:
"completeness": <0.0-1.0>,
importance": <0.0-1.0>,
"accuracy": <0.0-1.0>,
summarizing findings"; "i <0.0-1.0>,
ispecification cectooffnniigg findings"; <0.0-1.0>
PAPER: [full PAPER text)
CANDIDATE FINDINGS:
[candidate findings section)
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L.4 TASK 13 PROMPT FOR REFERENCE BASED SCORE
We present the detailed prompt used for - when serving as the evaluation judge for Task 13 (Study Methods Generation) in Table 67 We weighted rubric with the scores from the ISON output. We give greater weight to the first two criteria, because they carry more information, while the third serves only an auxiliary signal.
S raw =2 x methods_c beethodd_clarrtyy_dd__urrossss
+ x methods peeettoo..echhiiallll.....pprrrr
+ methods methods__epppe ottpoods___ppaaaaaoo___________e terms
We average the above score by 5 and multiply the result by 100 to scale it to a 100 range.
Table 67: Prompt for LLM-based Judge for Task 13: Study Methods Generation.
Prompt:
You are a strict policy-brief evaluator. Given the full scientific PAPER and the CANDIDATE's methods section, assign quality scores to three core aspects of how the methodology is described. Each score should be float between 0.0 and 1.0.
Criteria:
(I) clarity_ann __and_________ purpose Is the method described in clear, structured way that highlights what was done and why, rather than simply listing tools or data sources?
Is the level of technical detail appropriate for policy audience without excessive jargon, complexity, or irrelevant detail?
(2) technicality aaproppraprooreeessss
(3) explanation of_ terms Are technical terms, models, data sources explained in accessi- ble language and context without unexplained acronyms or unclear references?
Scoring Instructions:
Assign a score between 0.0 and 1.0 for each criterion.
A higher score means the section performs well on that criterion.
A lower score means the section is vague, overly technical, unexplained, or missing that dimension.
Strict-grading Instructions:
Score conservativel:: if unsure, choose the lower score.
Base each score only comparisons between PAPER and CANDIDATE.
Return exactly the ISON object below (no explanations, no extra keys).
Return only a JSON object in this format:
"clarity_and" : 10.000
<0.0-1.0>,
"techhical steechm.cclltyy.ppprorrrieeesss <0.0-1.0>, <0.0-1.0>
"explanations iatpinattioooooeteeeeeeeee <0.0..111
PAPER: (full PAPER text)
CANDIDATE METHOD:
(candidate methods section) |
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L.5 TASK 14 PROMPT FOR REFERENCE BASED SCORE
We present the detailed prompt used for Gemini-2.5-PP when serving as the evaluation judge for Task 14 (Policy Implications Generation) in Table 68 We average the scores from the JSON output and multiply the result by 100 to scale to a 0-100 range.
Table 68: Prompt for LLM-based Judge for Task 14: Policy Implications Generation.
Prompt:
You strict policy-brief evaluator. Given the full scientific PAPER and the CANDIDATE'S policy implications section, assign quality scores to the following four criteria.
Dimensions:
(I) accuracy Are the implications explicitly supported by the PAPER without speculative or
hallucinated claims?
(2) coverage Does the section capture all major implications stated the PAPER?
(3) conciseness and distinctness - Are the implications concise and non-redundant? Each
point should make a distinct contribution.
(4) alignment_ with papllpppeeeria_a jnttettteettttteee Does the implication reflect the PAPER's main message
or takeaway (e.g., recommendation, warning, scientific insight, call to awareness)?
Scoring Instructions:
Assign a score between 0.0 and 1.0 for each dimension.
A higher score means the section performs well on that dimension.
lower score means the section is vague, incorrect, redundant, or misaligned.
Strict-grading Instructions:
Score conservatively: if unsure, choose the lower score.
Base each score only on comparisons between PAPER and CANDIDATE. Return exactly the JSON object below (no explanations, no extra keys).
Return only JSON object in this format:
aacurracy": <0.0-1.0z,
"coveaaee": <0.0-1.0>,
ccnncsseness__ "concisenes__an__diittee cacisss_an__diii aand_disttictness"" :ttproteessssaaddiddidiaatttttt" <0.0-1.0>,
<0.0-1.0>
PAPER: [full PAPER text)
CANDIDATE IMPLICATION:
/candidate policy implications section)
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L.6 TASK 15 PROMPT FOR REFERENCE BASED SCORE
We present the detailed prompt used for Gemini-2.5-Pan when serving as the evaluation judge for Task 15 (Policy Brief Generation) in Table 69 We average the scores from the JSON output and multiply the result by 100 to scale it to a 1-100 range.
Table 69; Prompt for LLM-based Judge for Task 15: Policy Brief Generation.
Prompt:
You are strict policy-brief evaluator. Given the full scientifie PAPER, EXPERT-writeen reference brief, and CANDIDATE brief, grade the CANDIDATE on four dimensions and produce compact JSON report.
Evaluation dimensions & conservative 0-5 rubric
Start each score at 0 and add points only when the brief clearly meets the criterion. Reserve 4 or for near-flawless performance; means "solid but with notable gaps"; 2 or below signals clear problems.
= disastrous 1 = poor = fair 3 good 4 very good = excellent
(I) ContextualDepth: Does the CANDIDATE capture the study's essential quantitative findings, methods, and broader context (e.g., raw-material outlook, scenario count) without missing key facts or adding fluff?
(2) Hallucination Are all claims traceable to the PAPER (or universally known)? Deduct heavily for any unsupported number causal claim.
(3) Readability Tone: Is the brief concise, logically ordered, written in active voice, and appropriate for policymakers? Penalize lengthy sentences or jargon.
(4) Actionability: Are policy implications concrete, tied directly to evidence, and immediately
useful? Vague or speculative advice 2.
Output format (MUST valid JSON; numeric scores only, no prose):
"contextual_deptie depth": <0-5>, "hallucination "issiiioo_rs_r______ <0-5>, reedabiility_tone" <0-5>, "actionability"" <0-5>
Strict-grading instructions:
Score conservatively: if unsure, choose the lower score.
Base each score only comparisons between PAPER and CANDIDATE; EXPERT_ is
reference context.
Return exactly the ISON object above (no explanations, no extra keys).
PAPER: [full PAPER text)
EXPERT :XPERT_BRIEF: (expert-written brief))
CANDIDATE BRIEF: (candidate brief)
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L.7 TASK PROMPT FOR DATA CURATION
We present the detailed prompt used with GPT-o3 when curating the dataset for Task 5 in Table 770
Table 70: Prompt for the Data Curation in Task
Prompt:
You given scientific paper and corresponding policy brief. The policy brief includes four components:
1. Policy Problem
2. Scientific Research Findings
3. Scientific Research Study Methods
4. Policy Implications
Your task is to extract valuable, content-rich passages from the scientífic paper that correspond to each of these components, as reflected in the policy brief. Each sample should preferably contain three more coherent and consecutive sentences, copied verbatim from the scientific paper. However, shorter excerpts are acceptable they are highly informative. Avoid random, trivial, disjointed selections.
Assign one of the following five labels to each extracted sample:
(1) Policy Problem
(2) Scientific Research Findings
(3) Scientific Research Study Methods (c.g., experimental design, data sources, modeling, and
implementation details)
(4) Policy Implications
(5) None: for content unrelated the policy translation task (including acknowledgments,
author contributions, and institutional affiliations)
Return exactly:
sample for Policy Problem
5 samples for Scientific Research Findings
-5 samples for Scientific Research Study Methods
2 samples for Policy Implications
2 samples for None
Output the result as a JSON array of objects, each with the following fields:
"label"; one of ["Policy Problem". "Scientific Research Findings", "Scientific Research Study Methods", "Policy Implications", "None"]
"text": the extracted passage copied verbatim from the scientific paper
Do not paraphrase. Do not include commentary. Only output valid JSON array of labeled, verbatim text segments.
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L.8 TASK 11 PROMPT FOR DATA CURATION
We present the detailed prompt used with GPT-o3 when curating the dataset for Task in Table 7
Table 71: Prompt for the Data Curation in Task 11.
Prompt:
In the following, you will see three examples, Each example includes scientific research paper and a paragraph describing the policy problem that the research addresses, as written for policy brief.
Your task is to write a new policy problem paragraph for a different scientific paper that I will provide.
Note: I will also give you additional paragraph related to the policy problem for the new paper. You may refer to it for context, but it not the desired output.
Example 1:
Scientific paper: (scientific paper text)
Policy problem paragraph: (policy problem text]
Example 2:
Scientific paper: (scientific paper text)
Policy problem paragraph: (policy problem text]
Example 3:
Scientific paper: (scientific paper text)
Policy problem paragraph: (policy problem text)
New Paper:
(scientific paper text)
Related paragraph for policy problem: (related policy problem text) Policy problem paragraph:
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L.9 TASK 13 PROMPT FOR DATA CURATION
We present the detailed prompt used with GPT-o3 when curating the dataset for Task in Table
Table 72: Prompt for the Data Curation in Task 13.
Prompt:
In the following, you will see three examples. Each example includes scientific research paper and paragraph describing the scientific research study method that the research addresses, as written for policy brief.
Your task write new scientific research study method paragraph for different scientific paper that I will provide.
Note: will also give you additional paragraph related to the scientific research study method for the new paper. You may refer for context, but is not the desired output.
Example 1:
Scientific paper: (scientific paper text]
Scientific research study method paragraph: (scientific research study method text)
Example 2:
Scientific paper: (scientific paper text)
Scientific research study method paragraph: (scientific research study method text)
Example 3:
Scientific paper: (scientific paper text)
Scientific research study method paragraph: (scientific research study method text)
New Paper: (scientific paper text)
Related paragraph for scientific research study method: |scientific research study method text
Scientific research study method paragraph:
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L.10 TASK 16 PROMPT FOR DATA CURATION
We present the detailed prompt used with GPT-o3 when curating the dataset for Task in Table
Table 73: Prompt for the Data Curation in Task 16.
Prompt:
You given scientific research paper.
Your task is to generate ten query-answer pairs for the following binary classification task:
Determine whether a stated research finding is fully supported by the research results reported in the scientific paper.
Each query-answer pair must include:
1. research research_results Copy one or two consecutive paragraphs verbatim from the paper that present empirical findings, statistics,
observations.
research finding Write concise sentence that either:
Accurately follows from the results (SUPPORT)
Sounds plausible, but is not actually supported, misstates causal direction, overgeneralizes, or infers something beyond the evidence (CONTRADICT)
3. answer Either "SUPPORT" or "CONTRADICT"
Requirements:
Return exactly 10 entries in total.
Include 5 SUPPORT and CONTRADICT examples-no more, no fewer.
Use a different results passage for each entry do not reuse.
Make the distinction between SUPPORT and CONTRADICT subtle and challenging (e.g.,
include plausible misinterpretations, causal reversals, or logical overextensions).
Reproducce all paper text exactly as written-no paraphrasing, truncation, ellipses.
-uttput only valid JSON file containing a list of 10 dictionaries.
Each dictionary must contain exactly the following keys: "research, "eneearchhresiiss.. "research_fis ng", and "answer".
Output JSON format:
[[
"research results": "<verbatim paragraph(s)>",
"research finding": "<concise sentence>",
"answer": "SUPPORT" ''CONTRADICT"
]}
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Published as a conference paper at ICLR 2026
L.11 TASK 18 PROMPT FOR DATA CURATION
We present the detailed prompt used with GPT-o3 when curating the dataset for Task in Table 74
Table 74: Prompt for the Data Curation in Task 18.
Prompt:
Please rewrite each of the following policy recommendations to express the opposite meaning as clearly and thoroughly as possible.
Policy Implications: (policy implication text)
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Published as a conference paper at ICLR 2026
M PROMPTS FOR SCI2POL-CORPUS CURATION
In this section, we present the detailed prompts used for Sci2Pol-Corpus curation: (i) the coarse- grained filtering prompt in Section 3.2 (TTalee 75p; (ii) the fine-grained filtering prompt in Section 3.2 (Table 76); and (iii) the in-context polishing prompt in Section 3.3 (Table
Table 75: Prompt for the Coarse-grained Filtering Step in Section 3,2
Prompt:
I will give you policy document and scientific article abstract. Your task is to evaluate whether the policy document is primarily about the scientific article it cites. Consider the following criteria:
The policy document must explicitly discuss the findings, methodology. or conclusions of the scientific article in detail.
1. Discussion of the Article's Content:
2. Policy Implications: The document must connect the scientific article to policy decisions, recommendations, or implications for policymakers.
3. Central Focus: The scientific article should be key focus of the policy document, rather than being just one of many references or a minor supporting citation.
Evaluation Steps:
Read the scientific article abstract understand its key points.
Analyze the policy document to determine whether engages with the article's content, its implications, and whether the article central focus.
Score the policy document on the following dimensions:
Relevance (0-5): How central is the scientific article the policy document? (0 only briefly mentioned, 5 = core focus).
Depth of Discussion (0-5): To what extent does the policy document engage with the scientific article's 's content (e.g., findings, methodology, conclusions)? (0 = minimal detail, 5 in-depth discussion).
Policy Connection (0-5): How well does the policy document translate the scientific article into policy implications or recommendations? (0 no connection, = strong, explicit connection).
Citation Frequency & Emphasis (0-5): How frequently and prominently is the article referenced in the policy document? (0 minor mention, 5 referenced throughout key source).
Provide final verdict whether the policy document is primarily about the seientific article. Return the output a valid JSON format.
Output JSON format:
[[
"verdict": "Yes" "No"",
"scores":
"relevance": 0-5,
"depth_of of ofw.sussssionniii.....ooooo 0-5,
"policy_come conilcyoconeetioon.. 0-5,
"citation wwwpruaece_eephhaaeeeeee 0-5 }.
"justification": "<3-5 sentence explanation>"
))
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Published as a conference paper at ICLR 2026
Table 76: Prompt for the Fine-grained Filtering Step Section 3.2
Prompt:
will give you a policy document and scientific article. Your task is to evaluate whether the
policy document is primarily about the scientific article. Consider the following criteria:
I. Discussion of the Article's Content: The policy document must explicitly discuss the findings, methodology. or conclusions of the scientific article in detail.
2. Policy Implications: The document must connect the scientific article to policy decisions, recommendations, or implications for policymakers.
3. Central Focus: The scientific article should be key focus of the policy document, rather than
being just one of many references or a minor supporting citation.
Evaluation Steps:
Read the scientific article understand its key points.
Analyze the policy document to determine whether engages with the article's 's content, its implications, and whether the article central focus.
Score the policy document on the following dimensions:
Relevance (0-5): How central is the scientific article the policy document? (0 only briefly mentioned, 5 = core focus).
Depth of Discussion (0-5): To what extent does the policy document engage with the scientific article's 's content (e.g., findings, methodology, conclusions)? (0 = minimal detail, 5 in-depth discussion).
Policy Connection (0-5): How well does the policy document translate the scientific article into policy implications or recommendations? (0 no connection, = strong, explicit connection).
Citation Frequency Emphasis (0-5): How frequently and prominently is the article referenced in the policy document? (0 minor mention, 5 referenced throughout
key source)
Document Similarity (0-5): Are the policy document and the scientific article almost exactly the same with only minor formatting differences? (0 = the text of policy document is very different from the scientific article, 5 the documents nearly identical). Provide final verdict on whether the policy document is primarily about the scientific article. Return the output valid JSON format.
Output Format:
[[
"verdict": "Yes" "No",
"scores":
"relevance": 0-5,
"depth_ _f discussion": 0-5,
gooliccycconeettie 0-5,
"citation rrequency_emphasee 0-5, "doc similaiillarit"":: 0-5
l.
"justification": "<3- sentence explanation>"
])
107

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Published as a conference paper at ICLR 2026
Table 77: Prompt for the In-context Polishing Step in Section 3.3
Prompt:
You professional editor specializing in policy briefs based scientific research. Use the sample scientific papers and their corresponding policy briefs as the standard for tone, structure, and formatting. Based on this reference, revise the draft policy brief for the target scientific paper. Ensure the revised brief is clear, accurate, concise, and policy-relevant, matching the quality of the samples.
Sample Scientific Paper [scientific paper text]
Sample Policy Brief 1: (policy brief text)
Sample Scientific Paper 2:
[scientific paper text)
Sample Policy 2: (policy brief text]
Sample Scientific Paper 3:
(scientific paper text)
Sample Policy Brief 3: (policy brief text)
Target Scientific Paper: (scientific paper text)
Draft Policy Brief: (policy brief text)
Respond with the revised policy brief' only, using the following format: Concise and precise, aligned with sample quality. Scientific Research Findings: Comprehensive and coherent (no bullet points), matching the structure of the original paper.
Policy Problem:
Scientific Research Study Methods: Narrative format (no point form), at the same level of
generality and technicality as the samples.
Policy Implications: Bullet points only; grounded strictly in the paper's findings without speculation or external examples.
Requirements:
Maintain the same functional length as the samples: each section should be long enough to reflect the depth and structure of the specific paper, not artificially extended or shortened to match sample length. Do not pad with filler, overexplain to match longer samples, or oversimplify to match shorter ones.
Use a professional, policy-oriented voice for scientifically literate audience.
Ensure strict factual alignment with the target scientific paper.
108


Reviews:
[{"review_id": "RjIsdp7njI", "reviewer": "Reviewer_UZs4", "summary": "The paper introduces Sci2Pol-Bench, a five-stage benchmark (Autocompletion, Understanding, Summarization, Generation, Verification) for turning scientific papers into policy briefs, and Sci2Pol-Corpus, a curated training set to fine-tune LLMs for this task. The benchmark draws on 85 expert-written paper–brief pairs from venues such as Nature Energy and Nature Climate Change and spans 18 tasks (mixed MCQ and open-ended). For generation tasks, the authors argue ROUGE/BERTScore are inadequate and instead use an LLM-as-judge rubric aligned to paper-grounded criteria. They evaluate 13 models, then fine-tune three open models on a 639-pair corpus created via citation linking over 5.6M policy documents, LLM-based filtering, and in-context polishing. They report consistent gains and claim a fine-tuned Gemma-27B can outperform GPT-4o/DeepSeek-V3 on their benchmark.", "questions": "Please address all issues raised in the Weaknesses section.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "* Clear problem framing & timely task: Scientific-to-policy translation is impactful and under-served in current evaluation suites; the five-stage taxonomy mirrors a realistic writing workflow and is easy to reason about.\n\n* Well-structured benchmark: 18 tasks with transparent construction and metrics per stage; the dataset composition and task table are clearly presented and grounded in real expert-authored briefs.\n\n* Thoughtful metric discussion: The paper diagnoses why ROUGE/BERTScore miss quality signals in policy writing and proposes rubric-driven LLM judging tailored to sections (problem, findings, methods, implications) and full briefs.\n\n* Empirical breadth: the authors evaluate13 diverse models, and results are summarized across taxonomy stages.", "weaknesses": "* Evaluation circularity & judge dependence: Many key results rely on proprietary LLM judges (Gemini-2.5-Pro for reference-free and reference-based scoring; o3 for filtering/polishing). This creates potential vendor/prompt-specific bias, and the headline claim “Gemma-27B-SFT surpasses GPT-4o/DeepSeek-V3” is only as strong as the judge’s alignment to human experts. Human evaluation (even on a subset) or judge triangulation (≥2 independent judges + agreement analysis) would strengthen credibility.\n\n* Risk of construction-time leakage/style imprinting: The in-context polishing step conditions on three expert exemplars from the 85-pair set; while Appendix H.6 reportedly checks leakage, the paper still asks the reader to trust that polishing didn’t imprint benchmark-specific style cues that advantage similar models/decoding. A stronger defense would include ablations that: (i) polish with held-out exemplars from outside the 85 pairs; (ii) evaluate on a disjoint external set of expert briefs.\n\n* Heuristic retrieval bias: Keeping only policy documents citing ≤3 papers biases toward single-paper briefs and may not reflect real policy synthesis, which often aggregates many sources. An analysis of how this heuristic shapes style/length and whether models trained on such pairs generalize to multi-source briefs is missing.\n\n* Limited human oversight in label generation: Several tasks use o3 to generate items/labels with “manual review” mentioned briefly (e.g., “correct three issues” in Lines 267-268). More details on reviewer counts, agreement, and correction rates would calibrate trust in classification ground truth.\n\n* Ethics & release clarity: The Ethics section is brief; given the policy domain, more detail is warranted on licensing, redistribution boundaries, bias auditing of the judge, and safeguards to prevent models from emitting over-confident policy prescriptions not supported by evidence. The paper mentions anonymous links and fair-use reasoning but lacks concrete release protocols and impact/risk assessments specific to policy advice.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "l3bBZ5VwFg", "reviewer": "Reviewer_RJb4", "summary": "The authors contribute an evaluation benchmark and corpus for the translation of scientific research papers to policy briefs: Sci2Pol-Bench and Sci2Pol-Corpus. For Sci2Pol-Bench, they detail a rigorous construction process of 18 tasks drawn from 85 expert-written paper-brief pairs, e.g., multiple-choice probes and open-ended generation tasks, along with associated metrics (e.g., F1, LLM-as-judge). For Sci2Pol-Corpus, they mine high-quality paper-brief pairs using a large-scale web dataset and LLM-as-judge filtering, obtaining 639 pairs. Lastly, they evaluate several frontier models and find finetuning on Sci2Pol-Corpus enables smaller models (e.g., Gemma-3-27B) to beat near-frontier models (DeepSeek-V3).", "questions": "Please see Weaknesses above for my concrete suggestions to improve the paper. These are more minor clarifications:\n- How did you source your experts, e.g., the political experts mentioned at line 202?\n- Do you use experts in all of your task constructions involving selection, e.g., Tasks 7-10 where you find summarizing paragraphs?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- Important setting and strong motivation: the conversion of scientific knowledge into content helpful to policymakers is increasingly important as AI matures and its real-world impact increases. Many simpler benchmarks are saturated by frontier models, and therefore contributing challenging expert-driven, real-world tasks is an important direction for the field.\n- The paper is clear and well-written.\n- The pipeline for evaluation and data curation is very meticulous. These details are clearly presented and will be very useful, such as to those tackling settings such as constructing RL environments for other domains.", "weaknesses": "The major challenge with new benchmarks is whether they are already saturated or likely to be saturated soon.\n* Could you provide a human baseline on Sci2Pol-Bench (e.g., with a policy expert)? Without it, it is hard to judge whether the benchmark is really unsaturated, or if there is just very high label noise or ambiguity.\n* Could you provide an analysis of the saturation of Sci2Pol-Bench with respect to some measure of model capability, such as pretraining FLOPs or release date (e.g., similar to the analysis of Epoch AI [1])? This would provide some idea of how soon this benchmark might be saturated.\n* [Less critical] If possible, could you report the agreement between expert human graders and the LLM grader function, e.g., for the Reference-Free Score in Tasks 7-10 or Reference-based Score in Tasks 11-15?\n\n[1] Kwa et al., Measuring AI Ability to Complete Long Tasks, 2025. https://arxiv.org/abs/2503.14499", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SgTQNzdewh", "reviewer": "Reviewer_F3Nb", "summary": "This paper introduces Sci2Pol-Bench and Sci2Pol-Corpus for evaluating and fine-tuning LLMs on policy brief generation from scientific papers. Sci2Pol-Bench contains 18 tasks across a five-stage taxonomy (Autocompletion, Understanding, Summarization, Generation, Verification) built on 85 expert-written paper-brief pairs. The authors evaluate 13 LLMs and reveal significant limitations. Sci2Pol-Corpus contains 639 paper-brief pairs curated from 5.6M policy documents. Fine-tuning three models on this corpus yields improvements, with Gemma-27B surpassing GPT-4o and DeepSeek-V3.", "questions": "1. Can you validate LLM judges with human experts? Provide inter-rater agreement between Gemini-2.5-Pro and multiple political science experts for Tasks 11-15.\n\n2. How does performance transfer across domains? Test fine-tuned models on policy briefs from economics, technology policy, or other areas beyond Nature/JHSB domains.\n\n3. What are common failure modes? Categorize errors (factual/structural/stylistic/logical) for top models on each task.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Addresses the gap between scientific research and policy communication through a well-designed five-stage taxonomy that mirrors real brief-writing workflows.\n\n2. Uses 85 expert-written paper-brief pairs from prestigious venues, ensuring reliable ground truth. The 18 tasks enable fine-grained evaluation across multiple capabilities.\n\n3. Three-stage pipeline (retrieval from 5.6M documents, LLM filtering, in-context polishing) with validation studies addressing information leakage concerns.", "weaknesses": "1. Only 85 test pairs and 639 training pairs, concentrated in climate/energy and health sociology. No evidence of generalization to other scientific domains (CS, math, humanities, economics).\n\n2. Tasks 7-15 use Gemini-2.5-Pro as judge, while GPT-o3 generates data for Tasks 5,11,13,16,18 and polishes all corpus documents. Models in GPT/Gemini families may benefit from evaluation alignment with their own generation patterns.\n\n3. Table 3 shows Gemma-27B-SFT outperforms GPT-4o, but lacks analysis of what was learned (factual grounding vs. style vs. pattern memorization). No qualitative comparison of base vs. fine-tuned outputs.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eynEQz6i6D", "reviewer": "Reviewer_6pgv", "summary": "The aim of the paper is to explore how LLMs can enhance by themselves their expertise in technology evaluation tasks.\nThe paper introduces a benchmarks and  a self-questioning and retrieval framework in which an LLM generates questions about patents, retrieves related information, and re-evaluates its own judgments to improve accuracy. That's the core idea. \nExperiments are conducted on a large dataset of ICT patents, using LLaMA as the main model, and investigate how question–answer pairs generated by different LLMs affect performance in pairwise similarity judgments between patents.", "questions": "See previous section", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper addresses an important problem, i.e., enabling LLMs to refine their own reasoning and knowledge without additional supervised data by assuming that knowledge is in but not easy to extract\n- A large patent dataset for pairwise comparisons.\n- The study of cross-LLM question transfer is novel (as far as I know as reviewer) and provides interesting observations.\n- The analysis reveals patterns of when the LLM has knowledge but it's not easy to be explored", "weaknesses": "The main limitation of the work concerns its restricted generalizability. Although the authors claim that the framework is broadly applicable, all experiments focus on ICT patent abstracts. The absence of tests in other domains—such as biomedical, legal, or scientific text—makes it difficult to assess how well the method transfers beyond this narrow context.\n\nA second limitation lies in the dependence on the chosen model. Most experiments use LLaMAs even if appendix mentions results with Qwen, Mixtral, and GPT-4o-mini. \n\nThe construction of the patent dataset and the definition of similarity might require a clearer exposition. Pairs are selected according to cosine similarity in a PATCER embedding space, but the paper does not seem to  specify the similarity thresholds, how duplicates were filtered, or how balanced the dataset is. Reporting these details—and testing sensitivity to the similarity cutoff—would make the empirical section more reproducible.\n\n\nThe use of the term “active learning” is potentially misleading. The proposed method involves self-questioning and retrieval, but not the core features of traditional active learning such as query selection with human feedback. Clarifying the terminology—or providing a formal mapping to the active learning paradigm—would prevent conceptual ambiguity.\n\nThe complexity of generated questions remains limited. Although the authors distinguish between surface-level and deeper questions, the generation process could be extended to produce more structured, compositional, or multi-hop queries that probe higher-order reasoning.\n\nRegarding labeling, the paper should explain more clearly how the binary yes/no decisions are obtained in the pairwise task. Specifically, it would be useful to state how the label mapping (e.g., 1 = same, 0 = different) and the confidence aggregation were handled, and whether equivalent formulations (“same?” vs. “different?”) produced consistent results.\n\nHow can authors be sure that used testset has not been used during the trining because many patent texts and scientific documents may already be part of pretraining corpora for large models. Is it possible that the evaluation data overlap with training data?\n\nIs it possibile to extend and generalize what here presented to other scenario (not just patents)?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)