GenAI4ELab/papercli-papers huggingface dataset:
SlavicNLP 2025 Shared Task: Detection and Classification of Persuasion Techniques in Parliamentary Debates and Social Media
Authors: Jakub Piskorski; Dimitar Dimitrov; Filip Dobranić; Marina Ernst; Jacek Haneczok; Ivan Koychev; Nikola Ljubešić; Michal Marcinczuk; Arkadiusz Modzelewski; Ivo Moravski; Roman Yangarber
Venue: ACL 2025 (Proceedings of the 10th Workshop on Slavic Natural Language Processing (Slavic NLP 2025))

Abstract: We present SlavicNLP 2025 Shared Task on Detection and Classification of Persuasion Techniques in Parliamentary Debates and Social Media. The task is structured into two subtasks: (1) Detection, to determine whether a given text fragment contains persuasion techniques, and (2) Classification, to determine for a given text fragment which persuasion techniques are present therein using a taxonomy of 25 persuasion technique taxonomy. The task focuses on two text genres, namely, parliamentary debates revolving around widely discussed topics, and social media, in five languages: Bulgarian, Croatian, Polish, Russian and Slovene. This task contributes to the broader effort of detecting and understanding manipulative attempts in various contexts. There were 15 teams that registered to participate in the task, of which 9 teams submitted a total of circa 220 system responses and described their approaches in 9 system description papers.
Enhancing Retrieval for ESGLLM via ESG-CID: A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS
Authors: Shafiuddin Rehan Ahmed; Ankit Shah; Quan Hung Tran; Vivek Khetan; Sukryool Kang; Ankit Mehta; Yujia Bao; Wei Wei
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: Climate change has intensified the need for transparency and accountability in organizational practices, making Environmental, Social, and Governance (ESG) reporting increasingly crucial. Frameworks like the Global Reporting Initiative (GRI) and the new European Sustainability Reporting Standards (ESRS) aim to standardize ESG reporting, yet generating comprehensive reports remains challenging due to the considerable length of ESG documents and variability in company reporting styles. To facilitate ESG report automation, Retrieval-Augmented Generation (RAG) systems can be employed, but their development is hindered by a lack of labeled data suitable for training retrieval models. In this paper, we leverage an underutilized source of weak supervision—the disclosure content index found in past ESG reports—to create a comprehensive dataset, ESG-CID, for both GRI and ESRS standards. By extracting mappings between specific disclosure requirements and corresponding report sections, and refining them using a Large Language Model as a judge, we generate a robust training and evaluation set. We benchmark popular embedding models on this dataset and show that fine-tuning BERT-based models can outperform commercial embeddings and leading public models, even under temporal data splits for cross-report style transfer from GRI to ESRS.
Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models
Authors: Marianne Chuang; Gabriel Chuang; Cheryl Chuang; John Chuang
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: We study the use of large language models (LLMs) to both evaluate and greenwash corporate climate disclosures. First, we investigate the use of the LLM-as-a-Judge (LLMJ) methodology for scoring company-submitted reports on emissions reduction targets and progress. Second, we probe the behavior of an LLM when it is prompted to greenwash a response subject to accuracy and length constraints. Finally, we test the robustness of the LLMJ methodology against responses that may be greenwashed using an LLM. We find that two LLMJ scoring systems, numerical rating and pairwise comparison, are effective in distinguishing high-performing companies from others, with the pairwise comparison system showing greater robustness against LLM-greenwashed responses.
Bridging AI and Carbon Capture: A Dataset for LLMs in Ionic Liquids and CBE Research
Authors: Sougata Saha; Gaurab Sarkar
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: Large Language Models (LLMs) have demonstrated exceptional performance in general knowledge and reasoning tasks across various domains. However, their effectiveness in specialized scientific fields like Chemical and Biological Engineering (CBE) remains underexplored. Addressing this gap requires robust evaluation benchmarks that assess both knowledge and reasoning capabilities in these niche areas, which are currently lacking. To bridge this divide, we present a comprehensive empirical analysis of LLM reasoning capabilities in CBE, with a focus on Ionic Liquids (ILs) for carbon sequestration—an emerging solution for mitigating global warming. We develop and release an expert-curated dataset of 5,920 examples designed to benchmark LLMs’ reasoning in this domain. The dataset incorporates varying levels of difficulty, balancing linguistic complexity and domain-specific knowledge. Using this dataset, we evaluate three open-source LLMs with fewer than 10 billion parameters. Our findings reveal that while smaller general-purpose LLMs exhibit basic knowledge of ILs, they lack the specialized reasoning skills necessary for advanced applications. Building on these results, we discuss strategies to enhance the utility of LLMs for carbon capture research, particularly using ILs. Given the significant carbon footprint of LLMs, aligning their development with IL research presents a unique opportunity to foster mutual progress in both fields and advance global efforts toward achieving carbon neutrality by 2050. Dataset link: https://github.com/sougata-ub/llms_for_ionic_liquids
Applying the Character-Role Narrative Framework with LLMs to Investigate Environmental Narratives in Scientific Editorials and Tweets
Authors: Francesca Grasso; Stefano Locci; Manfred Stede
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: Communication aiming to persuade an audience uses strategies to frame certain entities in ‘character roles’ such as hero, villain, victim, or beneficiary, and to build narratives around these ascriptions. The Character-Role Framework is an approach to model these narrative strategies, which has been used extensively in the Social Sciences and is just beginning to get attention in Natural Language Processing (NLP). This work extends the framework to scientific editorials and social media texts within the domains of ecology and climate change. We identify characters’ roles across expanded categories (human, natural, instrumental) at the entity level, and present two annotated datasets: 1,559 tweets from the Ecoverse dataset and 2,150 editorial paragraphs from Nature & Science. Using manually annotated test sets, we evaluate four state-of-the-art Large Language Models (LLMs) (GPT-4o, GPT-4, GPT-4-turbo, LLaMA-3.1-8B) for character-role detection and categorization, with GPT-4 achieving the highest agreement with human annotators. We then apply the best-performing model to automatically annotate the full datasets, introducing a novel entity-level resource for character-role analysis in the environmental domain.
Integrating Expert Labels into LLM-based Emission Goal Detection: Example Selection vs Automatic Prompt Design
Authors: Marco Wrzalik; Adrian Ulges; Anne Uersfeld; Florian Faust; Viola Campos
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies’ progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategies of (1) a dynamic selection of few-shot examples and (2) the automatic optimization of the prompt by the LLM itself. Our findings on a public dataset of 769 climate-related passages from real-world business reports indicate that automatic prompt optimization is the superior approach, while combining both methods provides only limited benefit. Qualitative results indicate that optimized prompts do indeed capture many intricacies of the targeted emission goal extraction task.
ClimateIE: A Dataset for Climate Science Information Extraction
Authors: Huitong Pan; Mustapha Adamu; Qi Zhang; Eduard Dragut; Longin Jan Latecki
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: The rapid growth of climate science literature necessitates advanced information extraction (IE) systems to structure knowledge for researchers and policymakers. We introduce ClimateIE, a novel framework combining taxonomy-guided large language model (LLM) annotation with expert validation to address three core tasks: climate-specific named entity recognition, relationship extraction, and entity linking. Our contributions include: (1) the ClimateIE-Corpus—500 climate publications annotated via a hybrid human-AI pipeline with mappings to the extended GCMD+ taxonomy; (2) systematic evaluation showing Llama-3.3-70B achieves state-of-the-art performance (strict F1: 0.378 NER, 0.367 EL), outperforming larger commercial models (GPT-4o) and domain-adapted baselines (ClimateGPT) by 11-58%; and (3) analysis revealing critical challenges in technical relationship extraction (MountedOn: 0.000 F1) and emerging concept linking (26.4% unlinkable entities). Upon acceptance, we will release the corpus, toolkit, and guidelines to advance climate informatics, establishing benchmarks for NLP in Earth system science and underscoring the need for dynamic taxonomy governance and implicit relationship modeling.
Biodiversity ambition analysis with Large Language Models
Authors: Stefan Troost; Roos Immerzeel; Christoph Krueger
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: The Kunming-Montreal Global Biodiversity Framework (GBF) has 23 action-oriented global targets for urgent action over the decade to 2030. Parties committing themselves to the targets set by the GBF are required to share their national targets and biodiversity plans. In a case study on the GBF target to reduce pollution risks, we analyze the commitments of 110 different Parties, in 6 different languages. Obtaining satisfactory results for this target, we argue that using Generative AI can be very helpful under certain conditions, and it is a relatively small step to scale up such an analysis for other GBF targets.
AI and Climate Change Discourse: What Opinions Do Large Language Models Present?
Authors: Marcelo Sartori Locatelli; Pedro Dutenhefner; Arthur Buzelin; Pedro Loures Alzamora; Yan Aquino; Pedro Augusto Torres Bento; Samira Malaquias; Victoria Estanislau; Caio Santana; Lucas Dayrell; Marisa Affonso Vasconcelos; Wagner Meira Jr.; Virgilio Almeida
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: Large Language Models (LLMs) are increasingly used in applications that shape public discourse, yet little is known aboutwhether they reflect distinct opinions on global issues like climate change. This study compares climate change-relatedresponses from multiple LLMs with human opinions collected through the People’s Climate Vote 2024 survey (UNDP – UnitedNations Development Programme and Oxford, 2024). We compare country and LLM”s answer probability distributions and apply Exploratory Factor Analysis (EFA) to identify latent opinion dimensions. Our findings reveal that while LLM responsesdo not exhibit significant biases toward specific demographic groups, they encompass a wide range of opinions, sometimesdiverging markedly from the majority human perspective.
Evaluating Retrieval Augmented Generation to Communicate UK Climate Change Information
Authors: Arjun Biswas; Hatim Chahout; Tristan Pigram; Hang Dong; Hywel T.P. Williams; Fai Fung; Hailun Xie
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: There is a huge demand for information about climate change across all sectors as societies seek to mitigate and adapt to its impacts. However, the volume and complexity of climate information, which takes many formats including numerical, text, and tabular data, can make good information hard to access. Here we use Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to create an AI agent that provides accurate and complete information from the United Kingdom Climate Projections 2018 (UKCP18) data archive. To overcome the problematic hallucinations associated with LLMs, four phases of experiments were performed to optimize different components of our RAG framework, combining various recent retrieval strategies. Performance was evaluated using three statistical metrics (faithfulness, relevance, coverage) as well as human evaluation by subject matter experts. Results show that the best model significantly outperforms a generic LLM (GPT-3.5) and has high-quality outputs with positive ratings by human experts. The UKCP Chatbot developed here will enable access at scale to the UKCP18 climate archives, offering an important case study of using RAG-based LLM systems to communicate climate information.
An Automated LLM-based Pipeline for Asset-Level Database Creation to Assess Deforestation Impact
Authors: Avanija Menon; Ovidiu Serban
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: The European Union Deforestation Regulation (EUDR) requires companies to prove their products do not contribute to deforestation, creating a critical demand for precise, asset-level environmental impact data. Current databases lack the necessary detail, relying heavily on broad financial metrics and manual data collection, which limits regulatory compliance and accurate environmental modeling. This study presents an automated, end-to-end data extraction pipeline that uses LLMs to create, clean, and validate structured databases, specifically targeting sectors with a high risk of deforestation. The pipeline introduces Instructional, Role-Based, Zero-Shot Chain-of-Thought (IRZ-CoT) prompting to enhance data extraction accuracy and a Retrieval-Augmented Validation (RAV) process that integrates real-time web searches for improved data reliability. Applied to SEC EDGAR filings in the Mining, Oil & Gas, and Utilities sectors, the pipeline demonstrates significant improvements over traditional zero-shot prompting approaches, particularly in extraction accuracy and validation coverage. This work advances NLP-driven automation for regulatory compliance, CSR (Corporate Social Responsibility), and ESG, with broad sectoral applicability.
Detecting Hyperpartisanship and Rhetorical Bias in Climate Journalism: A Sentence-Level Italian Dataset
Authors: Michele Joshua Maggini; Davide Bassi; Pablo Gamallo
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: We present the first Italian dataset for joint hyperpartisan and rhetorical bias detection in climate change discourse. The dataset comprises 48 articles (1,010 sentences) from far-right media outlets, annotated at sentence level for both binary hyperpartisan classification and a fine-grained taxonomy of 17 rhetorical biases. Our annotation scheme achieves a Cohen’s kappa agreement of 0.63 on the gold test set (173 sentences), demonstrating the complexity and reliability of the task. We conduct extensive analysis revealing significant correlations between hyperpartisan content and specific rhetorical techniques, particularly in climate change, Euroscepticism, and green policy coverage. To the best of our knowledge, we are the first to tackle hyperpartisan detection related to logical fallacies. Indeed, we studied their correlation. Moreover, up to our knowledge no previous work focused on hyperpartisan at sentence level. Our experiments with state-of-the-art language models (GPT-4o-mini) and Italian BERTbase models establish strong baselines for both tasks, while highlighting the challenges in detecting subtle manipulation strategies applied with rhetorical biases. To ensure reproducibility while addressing copyright concerns, we release article URLs, article id and paragraph’s number alongside comprehensive annotation guidelines. This resource advances research in cross-lingual propaganda detection and provides insights into the rhetorical strategies employed in Italian climate change discourse. We provide the code and the dataset to reproduce our results: https://anonymous.4open.science/r/Climate_HP-RB-D5EF/README.md
Scaling Species Diversity Analysis in Carbon Credit Projects with Large-Context LLMs
Authors: Jessica Walkenhorst; Colin McCormick
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: Reforestation and revegetation projects can help mitigate climate change because plant growth removes CO2 from the air. However, the use of non-native species and monocultures in these projects may negatively affect biodiversity. Here, we describe a data pipeline to extract information about species that are planted or managed in over 1,000 afforestation/reforestation/revegetation and improved forest management projects, based on detailed project documentation. The pipeline leverages a large-context LLM and results in a macro-averaged recall of 79% and a macro-averaged precision of 89% across all projects and species.
ClimateEval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change
Authors: Murathan Kurfali; Shorouq Zahra; Joakim Nivre; Gabriele Messori
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: ClimateEval is a comprehensive benchmark designed to evaluate natural language processing models across a broad range of tasks related to climate change. ClimateEval aggregates existing datasets along with a newly developed news classification dataset, created specifically for this release. This results in a benchmark of 25 tasks based on 13 datasets, covering key aspects of climate discourse, including text classification, question answering, and information extraction. Our benchmark provides a standardized evaluation suite for systematically assessing the performance of large language models (LLMs) on these tasks. Additionally, we conduct an extensive evaluation of open-source LLMs (ranging from 2B to 70B parameters) in both zero-shot and few-shot settings, analyzing their strengths and limitations in the domain of climate change.
Bidirectional Topic Matching: Quantifying Thematic Intersections Between Climate Change and Climate Mitigation News Corpora Through Topic Modelling
Authors: Raven Adam; Marie Kogler
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: Bidirectional Topic Matching (BTM) is a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate various topic modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet Allocation (LDA). It employs a dual-model approach, training separate topic models for each corpus and applying them reciprocally to enable comprehensive cross-corpus comparisons. This methodology facilitates the identification of shared themes and unique topics, providing nuanced insights into thematic relationships. A case study on climate news articles illustrates BTM’s utility by analyzing two distinct corpora: news coverage on climate change and articles focused on climate mitigation. The results reveal significant thematic overlaps and divergences, shedding light on how these two aspects of climate discourse are framed in the media.
CPIQA: Climate Paper Image Question Answering Dataset for Retrieval-Augmented Generation with Context-based Query Expansion
Authors: Rudra Mutalik; Abiram Panchalingam; Loitongbam Gyanendro Singh; Timothy J. Osborn; Ed Hawkins; Stuart E. Middleton
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: Misinformation about climate science is a serious challenge for our society. This paper introduces CPIQA (Climate Paper Image Question-Answering), a new question-answer dataset featuring 4,551 full-text open-source academic papers in the area of climate science with 54,612 GPT-4o generated question-answer pairs. CPIQA contains four question types (numeric, figure-based, non-figure-based, reasoning), each generated using three user roles (expert, non-expert, climate sceptic). CPIQA is multimodal, incorporating information from figures and graphs with GPT-4o descriptive annotations. We describe Context-RAG, a novel method for RAG prompt decomposition and augmentation involving extracting distinct contexts for the question. Evaluation results for Context-RAG on the benchmark SPIQA dataset outperforms the previous best state of the art model in two out of three test cases. For our CPIQA dataset, Context-RAG outperforms our standard RAG baseline on all five base LLMs we tested, showing our novel contextual decomposition method can generalize to any LLM architecture. Expert evaluation of our best performing model (GPT-4o with Context-RAG) by climate science experts highlights strengths in precision and provenance tracking, particularly for figure-based and reasoning questions.
Robust Table Information Extraction from Sustainability Reports: A Time-Aware Hybrid Two-Step Approach
Authors: Hendrik Weichel; Martin Simon; Jörg Schäfer
Venue: ACL 2025 (Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025))

Abstract: The extraction of emissions-related information from annual reports has become increasingly important due to the Corporate Sustainability Reporting Directive (CSRD), which mandates greater transparency in sustainability reporting. As a result, information extraction (IE) methods must be robust, ensuring accurate retrieval while minimizing false values. While large language models (LLMs) offer potential for this task, their black-box nature and lack of specialization in table structures limit their robustness – an essential requirement in risk-averse domains. In this work, we present a two-step hybrid approach which optimizes both accuracy and robustness. More precisely, we combine a rule-based step for table IE with a regularized LLM-based step, both leveraging temporal prior knowledge. Our tests demonstrate the advantages of combining structured rules with LLMs. Furthermore, the modular design of our method allows for flexible adaptation to various IE tasks, making it a practical solution for industry applications while also serving as a scalable assistive tool for information extraction.