GenAI4ELab/papercli-papers huggingface dataset:
Dynamic Feature Fusion for Sign Language Translation Using HyperNetworks
Authors: Ruiquan Zhang; Rui Zhao; Zhicong Wu; Liang Zhang; Haoqi Zhang; Yidong Chen
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: This paper presents an efficient dual-stream early fusion method for sign language translation. Inspired by the brain’s ability to process color, shape, and motion simultaneously, the method explores complex dependencies between RGB and keypoint streams, improving speed and efficiency. A key challenge is extracting complementary features from both streams while ensuring global semantic consistency to avoid conflicts and improve generalization. To address this issue, we propose a hypernetwork-based fusion strategy that effectively extracts salient features from RGB and keypoint streams, alongside a partial shortcut connection training method to strengthen the complementary information between the dual streams. Additionally, we introduce self-distillation and SST contrastive learning to maintain feature advantages while aligning the global semantic space. Experiments show that our method achieves state-of-the-art performance on two public sign language datasets, reducing model parameters by about two-thirds.
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
Authors: Sonam Gupta; Yatin Nandwani; Asaf Yehudai; Dinesh Khandelwal; Dinesh Raghu; Sachindra Joshi
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Fine-tuning Large Language Models (LLMs) on specific datasets is a common practice to improve performance on target tasks. However, this performance gain often leads to overfitting, where the model becomes too specialized in either the task or the characteristics of the training data, resulting in a loss of generalization. This paper introduces Selective Self-to-Supervised Fine-Tuning (S3FT), a fine-tuning approach that achieves better performance than the standard supervised fine-tuning (SFT) while improving generalization.S3FT leverages the existence of multiple valid responses to a query.By utilizing the model’s correct responses, S3FT reduces model specialization during the fine-tuning stage. S3FT first identifies the correct model responses from the training set by deploying an appropriate judge. Then, it fine-tunes the model using the correct model responses and the gold response (or its paraphrase) for the remaining samples.The effectiveness of S3FT is demonstrated through experiments on mathematical reasoning, Python programming and reading comprehension tasks. The results show that standard SFT can lead to an average performance drop of up to 4.4 on multiple benchmarks, such as MMLU and TruthfulQA. In contrast, S3FT reduces this drop by half, i.e. 2.5, indicating better generalization capabilities than SFT while performing significantly better on the fine-tuning tasks.
MRE-MI: A Multi-image Dataset for Multimodal Relation Extraction in Social Media Posts
Authors: Shizhou Huang; Bo Xu; Changqun Li; Yang Yu; Xin Alex Lin
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Despite recent advances in Multimodal Relation Extraction (MRE), existing datasets and approaches primarily focus on single-image scenarios, overlooking the prevalent real-world cases where relationships are expressed through multiple images alongside text. To address this limitation, we present MRE-MI, a novel human-annotated dataset that includes both multi-image and single-image instances for relation extraction. Beyond dataset creation, we establish comprehensive baselines and propose a simple model named Global and Local Relevance-Modulated Attention Model (GLRA) to address the new challenges in multi-image scenarios. Our extensive experiments reveal that incorporating multiple images substantially improves relation extraction in multi-image scenarios. Furthermore, GLRA achieves state-of-the-art results on MRE-MI, demonstrating its effectiveness. The datasets and source code can be found at https://github.com/JinFish/MRE-MI.
Discrete Diffusion Language Model for Efficient Text Summarization
Authors: Do Huu Dat; Duc Anh Do; Anh Tuan Luu; Wray Buntine
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: While diffusion models excel at conditionally generating high-quality images, prior works in discrete diffusion models were not evaluated on conditional long-text generation. This work addresses the limitations of prior discrete diffusion models for conditional long-text generation, particularly in the long abstractive summarization task. Despite faster decoding speeds compared to autoregressive methods, previous discrete diffusion models failed on the abstractive summarization task due to the incompatibility between the backbone architectures and the random noising process. To overcome these challenges, we introduce a novel semantic-aware noising process that enables Transformer backbones to handle long sequences effectively. Additionally, we propose CrossMamba, an adaptation of the Mamba model to the encoder-decoder paradigm, which integrates seamlessly with the random absorbing noising process. Our approaches outperform existing discrete diffusion models on three benchmark summarization datasets: Gigaword, CNN/DailyMail, and Arxiv, while also achieving much faster inference speed compared to autoregressive models.
CAPE: A Chinese Dataset for Appraisal-based Emotional Generation in Large Language Models
Authors: June M. Liu; He Cao; Renliang Sun; Rui Wang; Yu Li; Jiaxing Zhang
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Generating emotionally appropriate responses in conversations with large language models presents a significant challenge due to the complexities of human emotions and cognitive processes, which remain largely underexplored in their critical role in social interactions. In this study, we introduce a two-stage automatic data generation framework to create CAPE, a Chinese dataset named Cognitive Appraisal theory-based Emotional corpus. This corpus facilitates the generation of dialogues with contextually appropriate emotional responses by accounting for diverse personal and situational factors. We propose two tasks utilizing this dataset: emotion prediction and next utterance prediction. Both automated and human evaluations demonstrate that agents trained on our dataset can deliver responses that are more aligned with human emotional expressions. Our study shows the potential for advancing emotional expression in conversational agents, paving the way for more nuanced and meaningful human-computer interactions.
Beyond Under-Alignment: Atomic Preference Enhanced Factuality Tuning for Large Language Models
Authors: Hongbang Yuan; Yubo Chen; Pengfei Cao; Zhuoran Jin; Kang Liu
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Large language models (LLMs) have achieved remarkable success but still tend to generate factually erroneous responses, a phenomenon known as hallucination. A recent trend is to use preference learning to fine-tune models to align with factuality. However, existing work primarily evaluates fine-tuned models on in-domain (ID) datasets and the factuality on out-of-domain (OOD) datasets remains underexplored. In this paper, we conduct a comprehensive evaluation of the factuality of different models tuned by various preference learning algorithms and demonstrate that their performance on OOD datasets either increases minimally or decreases. Subsequently, we reveal that the main cause of model’s failure to uphold factuality under a distribution shift is under-alignment, rather than over-alignment, by analyzing the token distribution shift of the models before and after tuning. Finally, we propose APEFT (Atomic Preference Enhanced Factuality Tuning), a framework that enhances model’s awareness of factuality at the granularity of individual facts. Extensive experiments demonstrate that APEFT improves model performance by an average of on both ID and OOD datasets, which is highly effective.
Weight-based Analysis of Detokenization in Language Models: Understanding the First Stage of Inference Without Inference
Authors: Go Kamoda; Benjamin Heinzerling; Tatsuro Inaba; Keito Kudo; Keisuke Sakaguchi; Kentaro Inui
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: According to the stages-of-inference hypothesis, early layers of language models map their subword-tokenized input, which does not necessarily correspond to a linguistically meaningful segmentation, to more meaningful representations that form the model’s “inner vocabulary”.Prior analysis of this *detokenization* stage has predominantly relied on probing and interventions such as path patching, which involve selecting particular inputs, choosing a subset of components that will be patched, and then observing changes in model behavior.Here, we show that several important aspects of the detokenization stage can be understood purely by analyzing model weights, without performing any model inference steps.Specifically, we introduce an analytical decomposition of first-layer attention in GPT-2.Our decomposition yields interpretable terms that quantify the relative contributions of position-related, token-related, and mixed effects.By focusing on terms in this decomposition, we discover weight-based explanations of attention bias toward close tokens and attention for detokenization.
DiPT: Enhancing LLM Reasoning through Diversified Perspective-Taking
Authors: Hoang Anh Just; Mahavir Dabas; Lifu Huang; Ming Jin; Ruoxi Jia
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Existing work on improving language model reasoning typically explores a single solution path, which can be prone to errors. Inspired by perspective-taking in social studies, this paper introduces DiPT, a novel approach that complements current reasoning methods by explicitly incorporating diversified viewpoints. This approach allows the model to gain a deeper understanding of the problem’s context and identify the most effective solution path during the inference stage. Additionally, it provides a general data-centric AI recipe for augmenting existing data to improve their quality for fine-tuning. Our empirical results demonstrate that DiPT can be flexibly integrated into existing methods that focus on a single reasoning approach, enhancing their reasoning performance and stability when presented with paraphrased problems. Furthermore, we illustrate improved context understanding by maintaining the model’s safe outputs against “jailbreaking” prompts intentionally designed to bypass safeguards built into deployed models. Lastly, we show that fine-tuning with data enriched with diverse perspectives can boost the reasoning capabilities of the model compared to fine-tuning with raw data alone.
SOLID: Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking Dialogs
Authors: Arian Askari; Roxana Petcu; Chuan Meng; Mohammad Aliannejadi; Amin Abolghasemi; Evangelos Kanoulas; Suzan Verberne
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Intent prediction in information-seeking dialogs is challenging and requires a substantial amount of data with human-labeled intents for effective model training. While Large Language Models (LLMs) have demonstrated effectiveness in generating synthetic data, existing methods typically rely on human feedback and are tailored to structured, task-oriented intents. In this paper, we leverage LLMs for zero-shot generation of large-scale, open-domain, intent-aware information-seeking dialogs to serve as training data for intent prediction models. We introduce SOLID, a method that generates dialogs turn by turn using novel self-seeding and multi-intent self-instructing strategies. Additionally, we propose SOLID-RL, a finetuned version that generates an entire dialog in one step using data created with SOLID. SOLID and SOLID-RL are each used to generate over 300k intent-aware dialogs, significantly surpassing the size of existing datasets. Experiments show that intent prediction models trained on sampled dialogs generated by SOLID and SOLID-RL outperform those trained solely on human-generated dialogs. Our findings demonstrate the potential of LLMs to expand training datasets, as they provide valuable resources for conversational agents across multiple tasks. Our self-seeding and self-instructing approaches are adaptable to various conversational data types and languages with minimal modifications.
ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classification
Authors: Yaswanth M; Vaibhav Singh; Ayush Maheshwari; Amrith Krishna; Ganesh Ramakrishnan
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction, via bootstrapping, to iteratively filter the generated rules and data. We induce rules via inductive generalisation of syntactic-ngrams, enabling us to capture a complementary source of supervision. These rules alone lead to performance gains in both, in-context learning (ICL) and fine-tuning (FT) settings. Similarly, use of augmented data from ARISE alone improves the performance for a model, outperforming configurations that rely on complex methods like contrastive learning. Further, our extensive experiments on various datasets covering three full-shot, eight few-shot and seven multilingual variant settings demonstrate that the rules and data we generate lead to performance improvements across these diverse domains and languages.
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context
Authors: Sangwon Yu; Ik-hwan Kim; Jongyoon Song; Saehyung Lee; Junsung Park; Sungroh Yoon
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Multi-hop reasoning, which requires multi-step reasoning based on the supporting documents within a given context, remains challenging for large language models (LLMs). LLMs often struggle to filter out irrelevant documents within the context, and their performance is sensitive to the absolute position of supporting documents within that context. In this paper, we identify an additional challenge: LLMs’ performance is also sensitive to the order, relative position, in which the supporting documents are presented. We refer to this as the misordered context problem. To address this issue, based on the theoretical approach, we propose a simple yet effective method called context repetition (CoRe), which involves prompting the model by repeatedly presenting the context. This ensures that certain contiguous reasoning segments within supporting documents are presented in the optimal order, effectively guiding the model’s reasoning in the appropriate direction. Applying CoRe, we improve the F1 score by up to 30%p on multi-hop QA tasks and increase accuracy by up to 70%p on a synthetic task. Additionally, CoRe helps mitigate the well-known “lost-in-the-middle” problem in LLMs and can be effectively combined with retrieval-based approaches utilizing Chain-of-Thought (CoT) reasoning.
Text Annotation via Inductive Coding: Comparing Human Experts to LLMs in Qualitative Data Analysis
Authors: Angelina Parfenova; Andreas Marfurt; Jürgen Pfeffer; Alexander Denzler
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: This paper investigates the automation of qualitative data analysis, focusing on inductive coding using large language models (LLMs). Unlike traditional approaches that rely on deductive methods with predefined labels, this research investigates the inductive process where labels emerge from the data. The study evaluates the performance of six open-source LLMs compared to human experts. As part of the evaluation, experts rated the perceived difficulty of the quotes they coded. The results reveal a peculiar dichotomy: human coders consistently perform well when labeling complex sentences but struggle with simpler ones, while LLMs exhibit the opposite trend. Additionally, the study explores systematic deviations in both human and LLM-generated labels by comparing them to the golden standard from the test set. While human annotations may sometimes differ from the golden standard, they are often rated more favorably by other humans. In contrast, some LLMs demonstrate closer alignment with the true labels but receive lower evaluations from experts.
Investigating the Zone of Proximal Development of Language Models for In-Context Learning
Authors: Peng Cui; Mrinmaya Sachan
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: In this paper, we introduce a learning analytics framework to analyze the in-context learning (ICL) behavior of large language models (LLMs) through the lens of the Zone of Proximal Development (ZPD), an established theory in educational psychology. ZPD delineates the range of tasks a learner can accomplish with appropriate guidance but not yet independently. We adapt this concept to ICL, measuring the ZPD of LLMs based on model performance on individual examples in different settings. Furthermore, we propose an item response theory (IRT) model to predict the distribution of zones for LLMs. Our findings reveal a series of intricate and multifaceted behaviors of ICL, providing new insights into understanding and leveraging this technique. Finally, we demonstrate how our framework can enhance LLM in both inference and fine-tuning scenarios: (1) By predicting a model’s zone distribution, we selectively apply ICL to queries that are most likely to benefit from demonstrations, achieving a better balance between inference cost and performance; (2) We propose a human-like curriculum for fine-tuning, which prioritizes examples within the model’s ZPD. The curriculum results in improved performance, and we explain its effectiveness through an analysis of the training dynamics of LLMs.
Breaking ReAct Agents: Foot-in-the-Door Attack Will Get You In
Authors: Itay Nakash; George Kour; Guy Uziel; Ateret Anaby Tavor
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Following the advancement of large language models (LLMs), the development of LLM-based autonomous agents has become prevalent.As a result, the need to understand the security vulnerabilities of these agents has become a critical task. We examine how ReAct agents can be exploited using a straightforward yet effective method we refer to as the foot-in-the-door attack.Our experiments show that indirect prompt injection attacks, prompted by harmless and unrelated requests (such as basic calculations) can significantly increase the likelihood of the agent performing subsequent malicious actions.Our results show that once a ReAct agent’s thought includes a specific tool or action, the likelihood of executing this tool in the subsequent steps increases significantly, as the agent seldom re-evaluates its actions. Consequently, even random, harmless requests can establish a ‘foot-in-the-door’, allowing an attacker to embed malicious instructions into the agent’s thought process, making it more susceptible to harmful directives.To mitigate this vulnerability, we propose implementing a simple reflection mechanism that prompts the agent to reassess the safety of its actions during execution, which can help reduce the success of such attacks.
As easy as PIE: understanding when pruning causes language models to disagree
Authors: Pietro Tropeano; Maria Maistro; Tuukka Ruotsalo; Christina Lioma
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Language Model (LM) pruning compresses the model by removing weights, nodes, or other parts of its architecture. Typically, pruning focuses on the resulting efficiency gains at the cost of effectiveness.However, when looking at how individual data pointsare affected by pruning, it turns out that a particular subset of data points always bears most of the brunt (in terms of reduced accuracy) when pruning,but this effect goes unnoticed when reporting the mean accuracy of all data points. These data points are called PIEs and have been studied in image processing, but not in NLP.In a study of various NLP datasets, pruning methods, and levels of compression, we find that PIEs impact inference quality considerably, regardless of class frequency, andthat BERT is more prone to this than BiLSTM. We also find that PIEs contain a high amount of data points that have the largest influence on how well the model generalises to unseen data. This means that when pruning, with seemingly moderate loss to accuracy across all data points, we in fact hurt tremendously those data points that matter the most. We trace what makes PIEs both hard and impactful to inference to their overall longer and more semantically complex text. These findings are novel and contribute to understanding how LMs are affected by pruning. The code is available at: https://github.com/pietrotrope/AsEasyAsPIE
Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction
Authors: Shengbin Yue; Ting Huang; Zheng Jia; Siyuan Wang; Shujun Liu; Yun Song; Xuanjing Huang; Zhongyu Wei
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper introduces a Multi-agent Legal Simulation Driver (MASER) to scalably generate synthetic data by simulating interactive legal scenarios. Leveraging real-legal case sources, MASER ensures the consistency of legal attributes between participants and introduces a supervisory mechanism to align participants’ characters and behaviors as well as addressing distractions. A Multi-stage Interactive Legal Evaluation (MILE) benchmark is further constructed to evaluate LLMs’ performance in dynamic legal scenarios. Extensive experiments confirm the effectiveness of our framework.
Exploring Backward Reasoning in Large Language Models
Authors: Leonardo Ranaldi; Giulia Pucci
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Multi-step reasoning through in-context learning strategies have been extensively explored, highlighting the abilities of Large Language Models (LLMs) to generate answers derived from step-by-step reasoning. These studies focus the attention on LLMs’ forward reasoning abilities epitomised in a series of general premises leading to a final solution. In this paper, by taking the reverse perspective, we study the backward reasoning abilities of LLMs, namely the inference that leads to the causal hypothesis. Behind formalising the backward problems, we analyse whether the LLMs are able to reason about the conclusion and reconstruct the original question that led to the delivery of the final answer. Operating with question-answering tasks involving symbolic reasoning, understanding, and commonsense abilities, we observe that the proposed models reveal robust comprehension capabilities managing different kinds of input; however, they are not always able to reason in the backward direction. Finally, to challenge this limitation, we demonstrate that instructing LLMs to generate the answer by reconsidering the structure of the problem allows for improved backward reasoning direction.