GenAI4ELab/papercli-papers huggingface dataset:
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution’s Characteristics
Authors: Lorenzo Jaime Yu Flores; Ori Ernst; Jackie CK Cheung
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Well-calibrated model confidence scores can improve the usefulness of text generation models. For example, users can be prompted to review predictions with low confidence scores, to prevent models from returning bad or potentially dangerous predictions. However, confidence metrics are not always well calibrated in text generation. One reason is that in generation, there can be many valid answers, which previous methods do not always account for. Hence, a confident model could assign probability to many sequences because they are all valid, and not because it is unsure about how to perform the task. We propose task-agnostic confidence metrics suited to generation, which rely solely on model probabilities without the need for further fine-tuning or heuristics. Using these, we are able to improve the calibration of BART and Flan-T5 on summarization, translation, and question answering datasets.
KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?
Authors: Tianshi Zheng; Weihan Li; Jiaxin Bai; Weiqi Wang; Yangqiu Song
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Retrieval-Augmented Generation (RAG) systems show remarkable potential as question answering tools in the K-12 Education domain, where knowledge is typically queried within the restricted scope of authoritative textbooks. However, discrepancies between these textbooks and the parametric knowledge inherent in Large Language Models (LLMs) can undermine the effectiveness of RAG systems. To systematically investigate RAG system robustness against such knowledge discrepancies, we introduce KnowShiftQA. This novel question answering dataset simulates these discrepancies by applying deliberate hypothetical knowledge updates to both answers and source documents, reflecting how textbook knowledge can shift. KnowShiftQA comprises 3,005 questions across five subjects, designed with a comprehensive question typology focusing on context utilization and knowledge integration. Our extensive experiments on retrieval and question answering performance reveal that most RAG systems suffer a substantial performance drop when faced with these knowledge discrepancies. Furthermore, questions requiring the integration of contextual (textbook) knowledge with parametric (LLM) knowledge pose a significant challenge to current LLMs.
Improving Parallel Sentence Mining for Low-Resource and Endangered Languages
Authors: Shu Okabe; Katharina Hämmerl; Alexander Fraser
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: While parallel sentence mining has been extensively covered for fairly well-resourced languages, pairs involving low-resource languages have received comparatively little attention.To address this gap, we present Belopsem, a benchmark of new datasets for parallel sentence mining on three language pairs where the source side is low-resource and endangered: Occitan-Spanish, Upper Sorbian-German, and Chuvash-Russian. These combinations also reflect varying linguistic similarity within each pair. We compare three language models in an established parallel sentence mining pipeline and apply two types of improvements to one of them, Glot500. We observe better mining quality overall by both applying alignment post-processing with an unsupervised aligner and using a cluster-based isotropy enhancement technique. These findings are crucial for optimising parallel data extraction for low-resource languages in a realistic way.
Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models’ Uncertainty?
Authors: Jiayu Liu; Qing Zong; Weiqi Wang; Yangqiu Song
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: As large language models (LLMs) are increasingly used in high-stakes domains, accurately assessing their confidence is crucial. Humans typically express confidence through epistemic markers (e.g., “fairly confident”) instead of numerical values. However, it remains unclear whether LLMs consistently use these markers to reflect their intrinsic confidence due to the difficulty of quantifying uncertainty associated with various markers. To address this gap, we first define ***marker confidence*** as the observed accuracy when a model employs an epistemic marker. We evaluate its stability across multiple question-answering datasets in both in-distribution and out-of-distribution settings for open-source and proprietary LLMs. Our results show that while markers generalize well within the same distribution, their confidence is inconsistent in out-of-distribution scenarios. These findings raise significant concerns about the reliability of epistemic markers for confidence estimation, underscoring the need for improved alignment between marker based confidence and actual model uncertainty. Our code is available at https://github.com/HKUST-KnowComp/MarCon.
Limited-Resource Adapters Are Regularizers, Not Linguists
Authors: Marcell Fekete; Nathaniel Romney Robinson; Ernests Lavrinovics; Djeride Jean-Baptiste; Raj Dabre; Johannes Bjerva; Heather Lent
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Cross-lingual transfer from related high-resource languages is a well-established strategy to enhance low-resource language technologies. Prior work has shown that adapters show promise for, e.g., improving low-resource machine translation (MT). In this work, we investigate an adapter souping method combined with cross-attention fine-tuning of a pre-trained MT model to leverage language transfer for three low-resource Creole languages, which exhibit relatedness to different language groups across distinct linguistic dimensions. Our approach improves performance substantially over baselines. However, we find that linguistic relatedness—or even a lack thereof—does not covary meaningfully with adapter performance. Surprisingly, our cross-attention fine-tuning approach appears equally effective with randomly initialized adapters, implying that the benefit of adapters in this setting lies in parameter regularization, and not in meaningful information transfer. We provide analysis supporting this regularization hypothesis. Our findings underscore the reality that neural language processing involves many success factors, and that not all neural methods leverage linguistic knowledge in intuitive ways.
LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks
Authors: Anna Bavaresco; Raffaella Bernardi; Leonardo Bertolazzi; Desmond Elliott; Raquel Fernández; Albert Gatt; Esam Ghaleb; Mario Giulianelli; Michael Hanna; Alexander Koller; Andre Martins; Philipp Mondorf; Vera Neplenbroek; Sandro Pezzelle; Barbara Plank; David Schlangen; Alessandro Suglia; Aditya K Surikuchi; Ece Takmaz; Alberto Testoni
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reproducibility in the case of proprietary models. We provide JUDGE-BENCH, an extensible collection of 20 NLP datasets with human annotations covering a broad range of evaluated properties and types of data, and comprehensively evaluate 11 current LLMs, covering both open-weight and proprietary models, for their ability to replicate the annotations. Our evaluations show substantial variance across models and datasets. Models are reliable evaluators on some tasks, but overall display substantial variability depending on the property being evaluated, the expertise level of the human judges, and whether the language is human or model-generated. We conclude that LLMs should be carefully validated against human judgments before being used as evaluators.
FocalPO: Enhancing Preference Optimizing by Focusing on Correct Preference Rankings
Authors: Tong Liu; Xiao Yu; Wenxuan Zhou; Jindong Gu; Volker Tresp
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Efficient preference optimization algorithms such as Direct Preference Optimization (DPO) have become a popular approach in aligning large language models (LLMs) with human preferences. These algorithms implicitly treat the LLM as a reward model, and focus on training it to correct misranked preference pairs. However, recent work (CITATION) empirically finds that DPO training rarely improves these misranked preference pairs, despite its gradient emphasizing on these cases. We introduce FocalPO, a DPO variant that instead down-weighs misranked preference pairs and prioritizes enhancing the model’s understanding of pairs that it can already rank correctly. Inspired by Focal Loss used in vision tasks, FocalPO achieves this by adding a modulating factor to dynamically scale DPO loss. Our experiment demonstrates that FocalPO surpasses DPO and its variants on popular benchmarks like Alpaca Eval 2.0 and Arena-Hard using Mistral-Base-7B and Llama-3-Instruct-8B, with the introduced hyperparameter fixed. Additionally, we empirically reveals how FocalPO affects training on correct and incorrect sample groups, further underscoring its effectiveness.
Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in LLMs
Authors: Megh Thakkar; Quentin Fournier; Matthew Riemer; Pin-Yu Chen; Amal Zouaq; Payel Das; Sarath Chandar
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these expert models are not either explicitly trained to be safe, or experience a loss in their safety abilities in the process, making them capable of generating harmful content. We observe that simple interpolation between the domain and alignment delta parameters leads to safer domain-specific models that preserve their utility. Building on this, we introduce MergeAlign, a simple, efficient, and effective model merging-based alignment method. We apply MergeAlign on Llama3 models that are experts in medicine and finance, obtaining substantial safety alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged, as well as the applicability of MergeAlign on more general code and math expert models using the Qwen-2.5 series of models. We hope our findings open new research avenues towards efficient development and deployment of safe expert LLMs.
Can Uniform Meaning Representation Help GPT-4 Translate from Indigenous Languages?
Authors: Shira Wein
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: While ChatGPT and GPT-based models are able to effectively perform many tasks without additional fine-tuning, they struggle with tasks related to extremely low-resource languages and indigenous languages. Uniform Meaning Representation (UMR), a semantic representation designed to capture the meaning of texts in many languages, is well-positioned to be leveraged in the development of low-resource language technologies. In this work, we explore the downstream utility of UMR for low-resource languages by incorporating it into GPT-4 prompts. Specifically, we examine the ability of GPT-4 to perform translation from three indigenous languages (Navajo, Arápaho, and Kukama), with and without demonstrations, as well as with and without UMR annotations. Ultimately, we find that in the majority of our test cases, integrating UMR into the prompt results in a statistically significant increase in performance, which is a promising indication of future applications of the UMR formalism.
Subword models struggle with word learning, but surprisal hides it
Authors: Bastian Bunzeck; Sina Zarrieß
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: We study word learning in subword and character language models with the psycholinguistic lexical decision task. While subword LMs struggle to discern words and non-words with high accuracy, character LMs solve this task easily and consistently. Only when supplied with further contexts do subword LMs perform similarly to character models. Additionally, when looking at word-level and syntactic learning trajectories, we find that both processes are separable in character LMs. Word learning happens before syntactic learning, whereas both occur simultaneously in subword LMs. This raises questions about the adequacy of subword LMs for modeling language acquisition and positions character LMs as a viable alternative to study processes below the syntactic level.
LLM as Entity Disambiguator for Biomedical Entity-Linking
Authors: Christophe Ye; Cassie S. Mitchell
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Entity linking involves normalizing a mention in medical text to a unique identifier in a knowledge base, such as UMLS or MeSH. Most entity linkers follow a two-stage process: first, a candidate generation step selects high-quality candidates, and then a named entity disambiguation phase determines the best candidate for final linking. This study demonstrates that leveraging a large language model (LLM) as an entity disambiguator significantly enhances entity linking models’ accuracy and recall. Specifically, the LLM disambiguator achieves remarkable improvements when applied to alias-matching entity linking methods. Without any fine-tuning, our approach establishes a new state-of-the-art (SOTA), surpassing previous methods on multiple prevalent biomedical datasets by up to 16 points in accuracy. We released our code on GitHub at https://github.com/ChristopheYe/llm_disambiguator
Towards Geo-Culturally Grounded LLM Generations
Authors: Piyawat Lertvittayakumjorn; David Kinney; Vinodkumar Prabhakaran; Donald Martin Jr.; Sunipa Dev
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Generative large language models (LLMs) have demonstrated gaps in diverse cultural awareness across the globe. We investigate the effect of retrieval augmented generation and search-grounding techniques on LLMs’ ability to display familiarity with various national cultures. Specifically, we compare the performance of standard LLMs, LLMs augmented with retrievals from a bespoke knowledge base (i.e., KB grounding), and LLMs augmented with retrievals from a web search (i.e., search grounding) on multiple cultural awareness benchmarks. We find that search grounding significantly improves the LLM performance on multiple-choice benchmarks that test propositional knowledge (e.g., cultural norms, artifacts, and institutions), while KB grounding’s effectiveness is limited by inadequate knowledge base coverage and a suboptimal retriever. However, search grounding also increases the risk of stereotypical judgments by language models and fails to improve evaluators’ judgments of cultural familiarity in a human evaluation with adequate statistical power. These results highlight the distinction between propositional cultural knowledge and open-ended cultural fluency when it comes to evaluating LLMs’ cultural awareness.
MUSTS: MUltilingual Semantic Textual Similarity Benchmark
Authors: Tharindu Ranasinghe; Hansi Hettiarachchi; Constantin Orasan; Ruslan Mitkov
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Predicting semantic textual similarity (STS) is a complex and ongoing challenge in natural language processing (NLP). Over the years, researchers have developed a variety of supervised and unsupervised approaches to calculate STS automatically. Additionally, various benchmarks, which include STS datasets, have been established to consistently evaluate and compare these STS methods. However, they largely focus on high-resource languages, mixed with datasets annotated focusing on relatedness instead of similarity and containing automatically translated instances. Therefore, no dedicated benchmark for multilingual STS exists. To solve this gap, we introduce the Multilingual Semantic Textual Similarity Benchmark (MUSTS), which spans 13 languages, including low-resource languages. By evaluating more than 25 models on MUSTS, we establish the most comprehensive benchmark of multilingual STS methods. Our findings confirm that STS remains a challenging task, particularly for low-resource languages.
Can Large Language Models Accurately Generate Answer Keys for Health-related Questions?
Authors: Davis Bartels; Deepak Gupta; Dina Demner-Fushman
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: The evaluation of text generated by LLMs remains a challenge for question answering, retrieval augmented generation (RAG), summarization, and many other natural language processing tasks. Evaluating the factuality of LLM generated responses is particularly important in medical question answering, where the stakes are high. One method of evaluating the factuality of text is through the use of information nuggets (answer keys). Nuggets are text representing atomic facts that may be used by an assessor to make a binary decision as to whether the fact represented by said nugget is contained in an answer. Although manual nugget extraction is expensive and time-consuming, recent RAG shared task evaluations have explored automating the nuggetization of text with LLMs. In this work, we explore several approaches to nugget generation for medical question answering and evaluate their alignment with expert human nugget generation. We find providing an example and extracting nuggets from an answer to be the best approach to nuggetization. While, overall, we found the capabilities of LLMs to distill atomic facts limited, Llama 3.3 performed the best out of the models we tested.
Literary Evidence Retrieval via Long-Context Language Models
Authors: Katherine Thai; Mohit Iyyer
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: How well do modern long-context language models understand literary fiction? We explore this question via the task of literary evidence retrieval, repurposing the RELiC dataset of Thai et al. (2022) to construct a benchmark where the entire text of a primary source (e.g., The Great Gatsby) is provided to an LLM alongside literary criticism with a missing quotation from that work. This setting, in which the model must generate the missing quotation, mirrors the human process of literary analysis by requiring models to perform both global narrative reasoning and close textual examination. We curate a high-quality subset of 292 examples through extensive filtering and human verification. Our experiments show that recent reasoning models, such as Gemini 2.5 Pro can exceed human expert performance (62.5% vs. 50% accuracy). In contrast, the best open-weight model achieves only 29.1% accuracy, highlighting a wide gap in interpretive reasoning between open and closed-weight models. Despite their speed and apparent accuracy, even the strongest models struggle with nuanced literary signals and overgeneration, signaling open challenges for applying LLMs to literary analysis. We release our dataset and evaluation code to encourage future work in this direction.
A Little Human Data Goes A Long Way
Authors: Dhananjay Ashok; Jonathan May
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Faced with an expensive human annotation process, creators of NLP systems increasingly turn to synthetic data generation. While this method shows promise, the extent to which synthetic data can replace human annotation is poorly understood. We investigate the use of synthetic data in Fact Verification (FV) and Evidence-based Question Answering (QA) by incrementally replacing human-generated data with synthetic points on eight diverse datasets. Strikingly, replacing up to 90% of the training data only marginally decreases performance, but replacing the final 10% leads to severe declines. We find that models trained on purely synthetic data can be improved by including as few as 125 human-generated data points. We show that matching the performance gain of a little human data requires an order of magnitude more synthetic data, and then estimate price ratios at which human annotation would be a more cost-effective solution. Our results suggest that even when human annotation at scale is infeasible, there is great value to having a small proportion of the dataset being human-generated.
Seeking Rational Demonstrations for Large Language Models: A Domain Generalization Approach to Unsupervised Cross-Domain Keyphrase Generation
Authors: Guangzhen Zhao; Yu Yao; Dechang Kong; Zhenjiang Dong
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Unsupervised cross-domain keyphrase generation is crucial in real-world natural language processing scenarios. However, the accuracy of up-to-date approaches is limited by the distribution shift between source and target domain, which stems from the cross-domain field. Large language models (LLMs) offer potential for the cross-domain keyphrase generation tasks due to their strong generalization abilities, facilitated by providing demonstrations relevant to the target task. Nevertheless, it is often difficult to obtain labeled samples from the target domain. To address this challenge, this paper aims to seek rational demonstrations from the source domain, thereby improving the LLMs’ ability in the unsupervised cross-domain keyphrase generation setting. Specifically, we design a novel domain-aware retrieval model on the source domain. Guided by insights from domain generalization theory, we introduce two generalization terms, one for cross-domain relevance and another for each domain consistency to better support retrieval of rational demonstrations. By the retrieved source-domain demonstrations and distance-based relevant score, the proposed approach achieves optimal accuracy. Comprehensive experiments on widely used cross-domain KG benchmarks demonstrate our approach’s state-of-the-art performance and effectiveness.