GenAI4ELab/papercli-papers huggingface dataset:
ReproHum #0033-05: Human Evaluation of Factuality from A Multidisciplinary Perspective
Authors: Andra-Maria Florescu; Marius Micluța-Câmpeanu; Stefana Arina Tabusca; Liviu P Dinu
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: The following paper is a joint contribution for the 2025 ReproNLP shared task, part of the ReproHum project. We focused on reproducing the human evaluation based on one criterion, namely, factuality of Scientific Automated Generated Systems from August et al. (2022). In accordance to the ReproHum guidelines, we followed the original study as closely as possible, with two human raters who coded 300 ratings each. Moreover, we had an additional study on two subsets of the dataset based on domain (medicine and physics) in which we employed expert annotators. Our reproduction of the factuality assessment found similar overall rates of factual inaccuracies across models. However, variability and weak agreement with the original model rankings suggest challenges in reliably reproducing results, especially in such cases when results are close.
ReproHum: #0744-02: Investigating the Reproducibility of Semantic Preservation Human Evaluations
Authors: Mohammad Arvan; Natalie Parde
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Reproducibility remains a fundamental challenge for human evaluation in Natural Language Processing (NLP), particularly due to the inherent subjectivity and variability of human judgments. This paper presents a reproduction study of the human evaluation protocol introduced by Hosking and Lapata (2021), which assesses semantic preservation in paraphrase generation models. By faithfully reproducing the original experiment with careful adaptation and applying the Quantified Reproducibility Assessment framework (Belz and Thomson, 2024a; Belz, 2022), we demonstrate strong agreement with the original findings, confirming the semantic preservation ranking among four paraphrase models. Our analyses reveal moderate inter-annotator agreement and low variability in key results, underscoring a good degree of reproducibility despite practical deviations in participant recruitment and platform. These findings highlight the feasibility and challenges of reproducing human evaluation studies in NLP. We discuss implications for improving methodological rigor, transparent reporting, and standardized protocols to bolster reproducibility in future human evaluations. The data and analysis scripts are publicly available to support ongoing community efforts toward reproducible evaluation in NLP and beyond.
ReproHum #0669-08: Reproducing Sentiment Transfer Evaluation
Authors: Kristýna Onderková; Mateusz Lango; Patrícia Schmidtová; Ondrej Dusek
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: We describe a reproduction of a human annotation experiment that was performed to evaluate the effectiveness of text style transfer systems (Reif et al., 2021). Despite our efforts to closely imitate the conditions of the original study, the results obtained differ significantly from those in the original study. We performed a statistical analysis of the results obtained, discussed the sources of these discrepancies in the study design, and quantified reproducibility. The reproduction followed the common approach to reproduction adopted by the ReproHum project.
ReproHum #0067-01: A Reproduction of the Evaluation of Cross-Lingual Summarization
Authors: Supryadi; Chuang Liu; Deyi Xiong
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Human evaluation is crucial as it offers a nuanced understanding that automated metrics often miss. By reproducing human evaluation, we can gain a better understanding of the original results. This paper is part of the ReproHum project, where our goal is to reproduce human evaluations from previous studies. We report the reproduction results of the human evaluation of cross-lingual summarization conducted by (CITATION). By comparing the original and reproduction studies, we find that our overall evaluation findings are largely consistent with those of the previous study. However, there are notable differences in evaluation scores between the two studies for certain model outputs. These discrepancies highlight the importance of carefully selecting evaluation methodologies and human annotators.
ReproHum #0729-04: Partial reproduction of the human evaluation of the MemSum and NeuSum summarisation systems
Authors: Simon Mille; Michela Lorandi
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: In this paper, we present our reproduction of part of the human evaluation originally carried out by Gu et al. (2022), as part of Track B of ReproNLP 2025. Four human annotators were asked to rank two candidate summaries according to their overall quality, given a reference summary shown alongside the two candidate summaries at evaluation time. We describe the original experiment and provide details about the steps we followed to carry out the reproduction experiment, including the implementation of some missing pieces of code. Our results, in particular the high coefficients of variation and low inter-annotator agreement, suggest a low level of reproducibility in the original experiment despite identical pairwise ranks. However, given the very small sample size (two systems, one rating), we remain cautious about drawing definitive conclusions.
Curse of bilinguality: Evaluating monolingual and bilingual language models on Chinese linguistic benchmarks
Authors: Yuwen Zhou; Yevgen Matusevych
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: We investigate cross-lingual transfer effects in large language models (LLMs) trained on two high-resource languages, English and Chinese. Four monolingual Chinese and four bilingual English–Chinese models are evaluated on two Chinese linguistic benchmarks. The monolingual models consistently outperform the bilingual ones on 12 out of 55 tasks, while the reverse is true for only 4 tasks, highlighting the prevalence of negative (rather than positive) transfer from English to Chinese. Additionally, we carry out a feature attribution analysis in a monolingual and a bilingual model, showing that the differences in their performance may be explained by more predictable attribution patterns in the monolingual model. Our findings have implications for the ongoing effort of training bilingual LLMs.
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework
Authors: Esteban Garces Arias; Hannah Blocher; Julian Rodemann; Meimingwei Li; Christian Heumann; Matthias Aßenmacher
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Open-ended text generation has become a prominent task in natural language processing due to the rise of powerful (large) language models. However, evaluating the quality of these models and the employed decoding strategies remains challenging due to trade-offs among widely used metrics such as coherence, diversity, and perplexity. This paper addresses the specific problem of multicriteria evaluation for open-ended text generation, proposing novel methods for both relative and absolute rankings of decoding methods. Specifically, we employ benchmarking approaches based on partial orderings and present a new summary metric to balance existing automatic indicators, providing a more holistic evaluation of text generation quality. Our experiments demonstrate that the proposed approaches offer a robust way to compare decoding strategies and serve as valuable tools to guide model selection for open-ended text generation tasks. We suggest future directions for improving evaluation methodologies in text generation and make our code, datasets, and models publicly available.
Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed versus Open LLMs for Automated Essay Scoring
Authors: Kezia Oketch; John P. Lalor; Yi Yang; Ahmed Abbasi
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Closed large language models (LLMs) such as GPT-4 have set state-of-the-art results across a number of NLP tasks and have become central to NLP and machine learning (ML)-driven solutions. Closed LLMs’ performance and wide adoption has sparked considerable debate about their accessibility in terms of availability, cost, and transparency. In this study, we perform a rigorous comparative analysis of eleven leading LLMs, spanning closed, open, and open-source LLM ecosystems, across text assessment and generation within automated essay scoring, as well as a separate evaluation on abstractive text summarization to examine generalization. Our findings reveal that for few-shot learning-based assessment of human generated essays, open LLMs such as Llama 3 and Qwen 2.5 perform comparably to GPT-4 in terms of predictive performance, with no significant differences in disparate impact scores when considering age- or race-related fairness. For summarization, we find that open models also match GPT-4 in ROUGE and METEOR scores on the CNN/DailyMail benchmark, both in zero- and few-shot settings. Moreover, Llama 3 offers a substantial cost advantage, being up to 37 times more cost-efficient than GPT-4. For generative tasks, we find that essays generated by top open LLMs are comparable to closed LLMs in terms of their semantic composition/embeddings and ML assessed scores. Our findings challenge the dominance of closed LLMs and highlight the democratizing potential of open LLMs, suggesting they can effectively bridge accessibility divides while maintaining competitive performance and fairness.
Prompt, Translate, Fine-Tune, Re-Initialize, or Instruction-Tune? Adapting LLMs for In-Context Learning in Low-Resource Languages
Authors: Christopher Toukmaji; Jeffrey Flanigan
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: LLMs are typically trained in high-resource languages, and tasks in lower-resourced languages tend to underperform the higher-resource language counterparts for in-context learning. Despite the large body of work on prompting settings, it is still unclear how LLMs should be adapted cross-lingually specifically for in-context learning in the low-resource target languages. We perform a comprehensive study spanning five diverse target languages, three base LLMs, and seven downstream tasks spanning over 4,100 GPU training hours (9,900+ TFLOPs) across various adaptation techniques: few-shot prompting, translate-test, fine-tuning, embedding re-initialization, and instruction fine-tuning. Our results show that the few-shot prompting and translate-test settings tend to heavily outperform the gradient-based adaptation methods. To better understand this discrepancy, we design a novel metric, Valid Output Recall (VOR), and analyze model outputs to empirically attribute the degradation of these trained models to catastrophic forgetting. To the extent of our knowledge, this is the largest study done on in-context learning for low-resource languages with respect to train compute and number of adaptation techniques considered. We make all our datasets and trained models available for public use.
Winning Big with Small Models: Knowledge Distillation vs. Self-Training for Reducing Hallucination in QA Agents
Authors: Ashley Lewis; Michael White; Jing Liu; Toshiaki Koike-Akino; Kieran Parsons; Ye Wang
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: The deployment of Large Language Models (LLMs) in customer support is constrained by hallucination—generating false information—and the high cost of proprietary models. To address these challenges, we propose a retrieval-augmented question-answering (QA) pipeline and explore how to balance human input and automation. Using a dataset of questions about a Samsung Smart TV user manual, we demonstrate that synthetic data generated by LLMs outperforms crowdsourced data in reducing hallucination in finetuned models. We also compare self-training (fine-tuning models on their own outputs) and knowledge distillation (fine-tuning on stronger models’ outputs, e.g., GPT-4o), and find that self-training achieves comparable hallucination reduction. We conjecture that this surprising finding can be attributed to increased exposure bias issues in the knowledge distillation case and support this conjecture with post hoc analysis. We also improve robustness to unanswerable questions and retrieval failures with contextualized “I don’t know” responses. These findings show that scalable, cost-efficient QA systems can be built using synthetic data and self-training with open-source models, reducing reliance on proprietary tools or costly human annotations.
Ad-hoc Concept Forming in the Game Codenames as a Means for Evaluating Large Language Models
Authors: Sherzod Hakimov; Lara Pfennigschmidt; David Schlangen
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: This study utilizes the game Codenames as a benchmarking tool to evaluate large language models (LLMs) with respect to specific linguistic and cognitive skills. LLMs play each side of the game, where one side generates a clue word covering several target words and the other guesses those target words. We designed various experiments by controlling the choice of words (abstract vs. concrete words, ambiguous vs. monosemic) or the opponent (programmed to be faster or slower in revealing words). Recent commercial and open-weight models were compared side-by-side to find out factors affecting their performance. The evaluation reveals details about their strategies, challenging cases, and limitations of LLMs.
Evaluating Intermediate Reasoning of Code-Assisted Large Language Models for Mathematics
Authors: Zena Al-Khalili; Nick Howell; Dietrich Klakow
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Assisting LLMs with code generation improved their performanceon mathematical reasoning tasks.However, the evaluation of code-assisted LLMs is generally restricted to execution correctness, lacking a rigorous evaluation of their generated programs.In this work, we bridge this gap by conducting an in-depth analysis of code-assisted LLMs generated programs in response to math reasoning tasks, with a focus on evaluating the soundness of the underlying reasoning processes. For this purpose, we assess the generations of five LLMs, on several math datasets, both manually and automatically, and propose a taxonomy of generated programs based on their logical soundness.Our findings show that the capabilities of models significantly impact the logic implemented to solve the problem. Closed-source LLMs ground their programs in mathematical concepts, whereas open-source models often resort to unsound reasoning, relying on memorized information and exhaustive searches. Furthermore, increasing the difficulty of problems decreases sound generations for all models, revealing a critical shortcoming of LLMs on complex mathematics, contrary to what accuracy metrics suggest.Our work highlights the need for more holistic evaluations of code-assisted LLMs beyond execution accuracy metrics, toward a better understanding of LLMs’ limits in the math domain.
From Calculation to Adjudication: Examining LLM Judges on Mathematical Reasoning Tasks
Authors: Andreas Stephan; Dawei Zhu; Matthias Aßenmacher; Xiaoyu Shen; Benjamin Roth
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: To reduce the need for human annotations, large language models (LLMs) have been proposed as judges of the quality of other candidate models. The performance of LLM judges is typically evaluated by measuring the correlation with human judgments on generative tasks such as summarization or machine translation. In contrast, we study LLM judges on mathematical reasoning tasks. These tasks require multi-step reasoning, and the correctness of their solutions is verifiable, enabling a more objective evaluation. We perform a detailed performance analysis and find that easy samples are easy to judge, and difficult samples are difficult to judge. Our analysis uncovers a strong correlation between judgment performance and the candidate model task performance, indicating that judges tend to favor higher-quality models even if their answer is incorrect. As a consequence, we test whether we can predict the behavior of LLM judges using simple features such as part-of-speech tags and find that we can correctly predict 70%-75% of judgments. We conclude this study by analyzing practical use cases, showing that LLM judges consistently detect the on-average better model but largely fail if we use them to improve task performance.
PersonaTwin: A Multi-Tier Prompt Conditioning Framework for Generating and Evaluating Personalized Digital Twins
Authors: Sihan Chen; John P. Lalor; Yi Yang; Ahmed Abbasi
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: While large language models (LLMs) afford new possibilities for user modeling and approximation of human behaviors, they often fail to capture the multidimensional nuances of individual users. In this work, we introduce PersonaTwin, a multi-tier prompt conditioning framework that builds adaptive digital twins by integrating demographic, behavioral, and psychometric data. Using a comprehensive data set in the healthcare context of more than 8,500 individuals, we systematically benchmark PersonaTwin against standard LLM outputs, and our rigorous evaluation unites state-of-the-art text similarity metrics with dedicated demographic parity assessments, ensuring that generated responses remain accurate and unbiased. Experimental results show that our framework produces simulation fidelity on par with oracle settings. Moreover, downstream models trained on persona-twins approximate models trained on individuals in terms of prediction and fairness metrics across both GPT-4o-based and Llama-based models. Together, these findings underscore the potential for LLM digital twin-based approaches in producing realistic and emotionally nuanced user simulations, offering a powerful tool for personalized digital user modeling and behavior analysis.
Coreference as an indicator of context scope in multimodal narrative
Authors: Nikolai Ilinykh; Shalom Lappin; Asad B. Sayeed; Sharid Loáiciga
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: We demonstrate that large multimodal language models differ substantially from humans in the distribution of coreferential expressions in a visual storytelling task. We introduce a number of metrics to quantify the characteristics of coreferential patterns in both human- and machine-written texts. Humans distribute coreferential expressions in a way that maintains consistency across texts and images, interleaving references to different entities in a highly varied way. Machines are less able to track mixed references, despite achieving perceived improvements in generation quality. Materials, metrics, and code for our study are available at https://github.com/GU-CLASP/coreference-context-scope.
PATCH! Psychometrics-AssisTed BenCHmarking of Large Language Models against Human Populations: A Case Study of Proficiency in 8th Grade Mathematics
Authors: Qixiang Fang; Daniel Oberski; Dong Nguyen
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Many existing benchmarks of large (multimodal) language models (LLMs) focus on measuring LLMs’ academic proficiency, often with also an interest in comparing model performance with human test takers’. While such benchmarks have proven key to the development of LLMs, they suffer from several limitations, including questionable measurement quality (e.g., Do they measure what they are supposed to in a reliable way?), lack of quality assessment on the item level (e.g., Are some items more important or difficult than others?) and unclear human population reference (e.g., To whom can the model be compared?). In response to these challenges, we propose leveraging knowledge from psychometrics—a field dedicated to the measurement of latent variables like academic proficiency—into LLM benchmarking. We make four primary contributions. First, we reflect on current LLM benchmark developments and contrast them with psychometrics-based test development. Second, we introduce PATCH: a novel framework for Psychometrics-AssisTed benCHmarking of LLMs. PATCH addresses the aforementioned limitations. In particular, PATCH enables valid comparison between LLMs and human populations.Third, we demonstrate PATCH by measuring several LLMs’ proficiency in 8th grade mathematics against 56 human populations. We show that adopting a psychometrics-based approach yields evaluation outcomes that diverge from those based on current benchmarking practices. Fourth, we release 4 high-quality datasets to support measuring and comparing LLM proficiency in grade school mathematics and science with human populations.
MCQFormatBench: Robustness Tests for Multiple-Choice Questions
Authors: Hiroo Takizawa; Saku Sugawara; Akiko Aizawa
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Multiple-choice questions (MCQs) are often used to evaluate large language models (LLMs). They measure LLMs’ general common sense and reasoning abilities, as well as their knowledge in specific domains such as law and medicine. However, the robustness of LLMs to various question formats in MCQs has not been thoroughly evaluated. While there are studies on the sensitivity of LLMs to input variations, research into their responsiveness to different question formats is still limited. In this study, we propose a method to construct tasks to comprehensively evaluate the robustness against format changes of MCQs by decomposing the answering process into several steps. Using this dataset, we evaluate nine LLMs, such as Llama3-70B and Mixtral-8x7B. We find the lack of robustness to differences in the format of MCQs. It is crucial to consider whether the format of MCQs influences their evaluation scores when assessing LLMs using MCQ datasets.
(Dis)improved?! How Simplified Language Affects Large Language Model Performance across Languages
Authors: Miriam Anschütz; Anastasiya Damaratskaya; Chaeeun Joy Lee; Arthur Schmalz; Edoardo Mosca; Georg Groh
Venue: ACL 2025 (Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²))

Abstract: Simplified language enhances the accessibility and human understanding of texts. However, whether it also benefits large language models (LLMs) remains underexplored. This paper extensively studies whether LLM performance improves on simplified data compared to its original counterpart. Our experiments span six datasets and nine automatic simplification systems across three languages. We show that English models, including GPT-4o-mini, show a weak generalization and exhibit a significant performance drop on simplified data. This introduces an intriguing paradox: simplified data is helpful for humans but not for LLMs. At the same time, the performance in non-English languages sometimes improves, depending on the task and quality of the simplifier. Our findings offer a comprehensive view of the impact of simplified language on LLM performance and uncover severe implications for people depending on simple language.