GenAI4ELab/papercli-papers huggingface dataset:
SafeQuant: LLM Safety Analysis via Quantized Gradient Inspection
Authors: Sindhu Padakandla; Sadbhavana Babar; Rathod Darshan D; Manohar Kaul
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Contemporary jailbreak attacks on Large Language Models (LLMs) employ sophisticated techniques with obfuscated content to bypass safety guardrails. Existing defenses either use computationally intensive LLM verification or require adversarial fine-tuning, leaving models vulnerable to advanced attacks. We introduce SafeQuant, a novel defense framework that leverages quantized gradient patterns to identify harmful prompts efficiently. Our key insight is that when generating identical responses like “Sure”, LLMs exhibit distinctly different internal gradient patterns for safe versus harmful prompts, reflecting conflicts with safety training. By capturing these patterns through selective gradient masking and quantization, SafeQuant significantly outperforms existing defenses across multiple benchmarks while maintaining model utility. The method demonstrates particular effectiveness against sophisticated attacks like WordGame prompts and persuasive adversarial attacks, achieving an F1-score of 0.80 on WordGame dataset and outperforming state-of-the-art (SoTA) methods like GradSafe by an absolute margin of 57%.
Exploring Large Language Models for Effective Rumor Detection on Social Media
Authors: Yirong Zeng; Xiao Ding; Bibo Cai; Ting Liu; Bing Qin
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: In this paper, we explore using Large Language Models (LLMs) for rumor detection on social media. It involves assessing the veracity of claims on social media based on social context (e.g., comments, propagation patterns). LLMs, despite their impressive capabilities in text-based reasoning tasks, struggle to achieve promising rumor detection performance when facing long structured social contexts. Our preliminary analysis shows that large-scale contexts hinder LLMs’ reasoning abilities, while moderate contexts perform better for LLMs, highlighting the need for refined contexts. Accordingly, we propose a semantic-propagation collaboration-base framework that integrates small language models (e.g., graph attention network) with LLMs for effective rumor detection. It models contexts by enabling text semantic and propagation patterns to collaborate through graph attention mechanisms, and reconstruct the context by aggregating attention values during inference. Also, a cluster-based unsupervised method to refine context is proposed for generalization. Extensive experiments demonstrate the effectiveness of proposed methods in rumor detection. This work bridges the gap for LLMs in facing long, structured data and offers a novel solution for rumor detection on social media.
No Simple Answer to Data Complexity: An Examination of Instance-Level Complexity Metrics for Classification Tasks
Authors: Ryan A. Cook; John P. Lalor; Ahmed Abbasi
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Natural Language Processing research has become increasingly concerned with understanding data quality and complexity at the instance level. Instance-level complexity scores can be used for tasks such as filtering out noisy observations and subsampling informative examples. However, there exists a diverse taxonomy of complexity metrics that can be used for a classification task, making metric selection itself a difficult task. We empirically examine the relationship between these metrics and find that simply storing training loss provides similar complexity rankings as other more computationally intensive techniques. Metric similarity allows us to subsample data with higher aggregate complexity along several metrics using a single a priori available meta-feature. Further, this choice of complexity metric does not impact demographic fairness, even in downstream predictions. Researchers should consider metric availability and similarity, as using the wrong metric or sampling strategy may hurt performance.
NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals
Authors: Neha Srikanth; Rachel Rudinger
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Decomposition of text into atomic propositions is a flexible framework allowing for the closer inspection of input and output text. We use atomic decomposition of hypotheses in two natural language reasoning tasks, traditional NLI and defeasible NLI, to form atomic sub-problems, or granular inferences that models must weigh when solving the overall problem. These atomic sub-problems serve as a tool to further understand the structure of both NLI and defeasible reasoning, probe a model’s consistency and understanding of different inferences, and measure the diversity of examples in benchmark datasets. Our results indicate that LLMs still struggle with logical consistency on atomic NLI and defeasible NLI sub-problems. Lastly, we identify critical atomic sub-problems of defeasible NLI examples, or those that most contribute to the overall label, and propose a method to measure the inferential consistency of a model, a metric designed to capture the degree to which a model makes consistently correct or incorrect predictions about the same fact under different contexts.
HISTOIRESMORALES: A French Dataset for Assessing Moral Alignment
Authors: Thibaud Leteno; Irina Proskurina; Antoine Gourru; Julien Velcin; Charlotte Laclau; Guillaume Metzler; Christophe Gravier
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Aligning language models with human values is crucial, especially as they become more integrated into everyday life. While models are often adapted to user preferences, it is equally important to ensure they align with moral norms and behaviours in real-world social situations. Despite significant progress in languages like English and Chinese, French has seen little attention in this area, leaving a gap in understanding how LLMs handle moral reasoning in this language. To address this gap, we introduce HistoiresMorales, a French dataset derived from MoralStories, created through translation and subsequently refined with the assistance of native speakers to guarantee grammatical accuracy and adaptation to the French cultural context. We also rely on annotations of the moral values within the dataset to ensure their alignment with French norms. HistoiresMorales covers a wide range of social situations, including differences in tipping practices, expressions of honesty in relationships, and responsibilities toward animals. To foster future research, we also conduct preliminary experiments on the alignment of multilingual models on French and English data and the robustness of the alignment. We find that while LLMs are generally aligned with human moral norms by default, they can be easily influenced with user-preference optimization for both moral and immoral data.
Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment
Authors: Kwanghee Choi; Eunjung Yeo; Kalvin Chang; Shinji Watanabe; David R Mortensen
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Allophony refers to the variation in the phonetic realization of a phoneme based on its phonetic environment. Modeling allophones is crucial for atypical pronunciation assessment, which involves distinguishing atypical from typical pronunciations. However, recent phoneme classifier-based approaches often simplify this by treating various realizations as a single phoneme, bypassing the complexity of modeling allophonic variation. Motivated by the acoustic modeling capabilities of frozen self-supervised speech model (S3M) features, we propose MixGoP, a novel approach that leverages Gaussian mixture models to model phoneme distributions with multiple subclusters. Our experiments show that MixGoP achieves state-of-the-art performance across four out of five datasets, including dysarthric and non-native speech. Our analysis further suggests that S3M features capture allophonic variation more effectively than MFCCs and Mel spectrograms, highlighting the benefits of integrating MixGoP with S3M features.
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search
Authors: Hanwen Du; Bo Peng; Xia Ning
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods train Reinforcement Learning (RL)-based agent with greedy action selection or sampling strategy, and may suffer from suboptimal conversational planning. To address this, we present a novel Monte Carlo Tree Search (MCTS)-based CRS framework SAPIENT. SAPIENT consists of a conversational agent (S-agent) and a conversational planner (S-planner). S-planner builds a conversational search tree with MCTS based on the initial actions proposed by S-agent to find conversation plans. The best conversation plans from S-planner are used to guide the training of S-agent, creating a self-training loop where S-agent can iteratively improve its capability for conversational planning. Furthermore, we propose an efficient variant SAPIENT for trade-off between training efficiency and performance. Extensive experiments on four benchmark datasets validate the effectiveness of our approach, showing that SAPIENT outperforms the state-of-the-art baselines. Our code and data are accessible through https://github.com/ninglab/SAPIENT.
Reliability of Topic Modeling
Authors: Kayla Schroeder; Zach Wood-Doughty
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Topic models allow researchers to extract latent factors from text data and use those variables in downstream statistical analyses. However, these methodologies can vary significantly due to initialization differences, randomness in sampling procedures, or noisy data. Reliability of these methods is of particular concern as many researchers treat learned topic models as ground truth for subsequent analyses. In this work, we show that the standard practice for quantifying topic model reliability fails to capture essential aspects of the variation in two widely-used topic models. Drawing from a extensive literature on measurement theory, we provide empirical and theoretical analyses of three other metrics for evaluating the reliability of topic models. On synthetic and real-world data, we show that McDonald’s 𝜔 provides the best encapsulation of reliability. This metric provides an essential tool for validation of topic model methodologies that should be a standard component of any topic model-based research.
Style Transfer with Multi-iteration Preference Optimization
Authors: Shuai Liu; Jonathan May
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Numerous recent techniques for text style transfer characterize their approaches as variants of reinforcement learning and preference optimization. In this work, we consider the relationship between these approaches and a class of optimization approaches developed primarily for (non-neural) statistical machine translation, formerly known as ‘tuning’. Inspired by these techniques from the past, we improve upon established preference optimization approaches, incorporating multiple iterations of exploration and optimization, and choosing contrastive examples by following a ‘hope’ vs ‘fear’ sampling strategy. Cognizant of the difference between machine translation and style transfer, however, we further tailor our framework with a new pseudo-parallel data generation method and a dynamic weighted reward aggregation method to tackle the lack of parallel data and the need for a multi-objective reward. We evaluate our model on two commonly used text style transfer datasets. Through automatic and human evaluation results we show the effectiveness and the superiority of our model compared to state-of-the-art baselines.
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations
Authors: Yichuan Li; Xinyang Zhang; Chenwei Zhang; Mao Li; Tianyi Liu; Pei Chen; Yifan Gao; Kyumin Lee; Kaize Ding; Zhengyang Wang; Zhihan Zhang; Jingbo Shang; Xian Li; Trishul Chilimbi
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Recommendation explanation systems have become increasingly vital with the widespread adoption of recommender systems. However, existing recommendation explanation evaluation benchmarks suffer from limited item diversity, impractical user profiling requirements, and unreliable and unscalable evaluation protocols. We present ALERT, a model-agnostic recommendation explanation evaluation benchmark. The benchmark comprises three main contributions: 1) a diverse dataset encompassing 15 Amazon e-commerce categories with 2,761 user-item interactions, incorporating implicit preferences through purchase histories;2) two novel LLM-powered automatic evaluators that enable scalable and human-preference aligned evaluation of explanations; and 3) a robust divide-and-aggregate approach that synthesizes multiple LLM judgments, achieving 70% concordance with expert human evaluation and substantially outperforming existing methods.ALERT facilitates comprehensive evaluation of recommendation explanations across diverse domains, advancing the development of more effective explanation systems.
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization
Authors: Yasir Khan; Xinlei Wu; Sangpil Youm; Justin Ho; Aryaan Mehboob Shaikh; Jairo Garciga; Rohan Sharma; Bonnie J Dorr
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-ofthe- art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures.
IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models
Authors: David Ifeoluwa Adelani; Jessica Ojo; Israel Abebe Azime; Jian Yun Zhuang; Jesujoba Oluwadara Alabi; Xuanli He; Millicent Ochieng; Sara Hooker; Andiswa Bukula; En-Shiun Annie Lee; Chiamaka Ijeoma Chukwuneke; Happy Buzaaba; Blessing Kudzaishe Sibanda; Godson Koffi Kalipe; Jonathan Mukiibi; Salomon Kabongo Kabenamualu; Foutse Yuehgoh; Mmasibidi Setaka; Lolwethu Ndolela; Nkiruka Odu; Rooweither Mabuya; Salomey Osei; Shamsuddeen Hassan Muhammad; Sokhar Samb; Tadesse Kebede Guge; Tombekai Vangoni Sherman; Pontus Stenetorp
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Despite the widespread adoption of Large language models (LLMs), their remarkable capabilities remain limited to a few high-resource languages. Additionally, many low-resource languages (e.g. African languages) are often evaluated only on basic text classification tasks due to the lack of appropriate or comprehensive benchmarks outside of high-resource languages. In this paper, we introduce IrokoBench—a human-translated benchmark dataset for 17 typologically-diverse low-resource African languages covering three tasks: natural language inference(AfriXNLI), mathematical reasoning(AfriMGSM), and multi-choice knowledge-based QA(AfriMMLU). We use IrokoBench to evaluate zero-shot, few-shot, and translate-test settings(where test sets are translated into English) across 10 open and four proprietary LLMs. Our evaluation reveals a significant performance gap between high-resource languages (such as English and French) and low-resource African languages. We observe a significant performance gap between open and proprietary models, with the highest performing open model, Gemma 2 27B only at 63% of the best-performing proprietary model GPT-4o performance. Machine translating the test set to English before evaluation helped to close the gap for larger models that are English-centric, like Gemma 2 27B and LLaMa 3.1 70B. These findings suggest that more efforts are needed to develop and adapt LLMs for African languages.
The Impact of Domain-Specific Terminology on Machine Translation for Finance in European Languages
Authors: Arturo Oncevay; Charese Smiley; Xiaomo Liu
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: Domain-specific machine translation (MT) poses significant challenges due to specialized terminology, particularly when translating across multiple languages with scarce resources. In this study, we present the first impact analysis of domain-specific terminology on multilingual MT for finance, focusing on European languages within the subdomain of macroeconomics. To this end, we construct a multi-parallel corpus from the European Central Bank, aligned across 22 languages. Using this resource, we compare open-source multilingual MT systems with large language models (LLMs) that possess multilingual capabilities. Furthermore, by developing and curating an English financial glossary, we propose a methodology to analyze the relationship between translation performance (into English) and the accuracy of financial term matching, obtaining significant correlation results. Finally, using the multi-parallel corpus and the English glossary, we automatically align a multilingual financial terminology, validating the English-Spanish alignments and incorporating them into our discussion. Our findings provide valuable insights into the current state of financial MT for European languages and offer resources for future research and system improvements.
Benchmarking Language Model Creativity: A Case Study on Code Generation
Authors: Yining Lu; Dixuan Wang; Tianjian Li; Dongwei Jiang; Sanjeev Khudanpur; Meng Jiang; Daniel Khashabi
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: As LLMs become increasingly prevalent, it is interesting to consider how “creative” these models can be. From cognitive science, creativity consists of at least two key characteristics: convergent thinking (purposefulness to achieve a given goal) and divergent thinking (adaptability to explore new environments or constraints) (CITATION). In this work, we introduce a framework for quantifying LLM creativity that incorporates the two design ingredients: (1) We introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions to a given problem by incrementally imposing new constraints on the previous solution, compelling LLMs to adopt new strategies. (2) We define NEOGAUGE, a metric that quantifies both convergent and divergent thinking in the generated creative responses by LLMs. We test the proposed framework on Codeforces problems, which serve as both a natural dataset for coding tasks and a collection of prior human solutions. We quantify NEOGAUGE for various proprietary and open-source models and find that even the most creative model, GPT-4, still falls short of demonstrating human-like creativity. We also experiment with advanced reasoning strategies (MCTS, self-correction, etc.) and observe no significant improvement in creativity. As a by-product of our analysis, we release NEOCODER dataset for reproducing our results on future models.
Have LLMs Reopened the Pandora’s Box of AI-Generated Fake News?
Authors: Xinyu Wang; Wenbo Zhang; Sai Koneru; Hangzhi Guo; Bonam Mingole; S. Shyam Sundar; Sarah Rajtmajer; Amulya Yadav
Venue: NAACL 2025 (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))

Abstract: With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of LLMs to produce convincing fake news at scale poses new challenges for both human and automated fake news detection systems. To address this gap, this paper presents the findings from a university-level competition that aimed to explore how LLMs can be used by humans to create fake news, and to assess the ability of human annotators and AI models to detect it. A total of 110 participants used LLMs to create 252 unique fake news stories, and 84 annotators participated in the detection tasks. Our findings indicate that LLMs are ~68% more effective at detecting real news than humans. However, for fake news detection, the performance of LLMs and humans remains comparable (~60% accuracy). Additionally, we examine the impact of visual elements (e.g., pictures) in news on the accuracy of detecting fake news stories. Finally, we also examine various strategies used by fake news creators to enhance the credibility of their AI-generated content. This work highlights the increasing complexity of detecting AI-generated fake news, particularly in collaborative human-AI settings.