GenAI4ELab/papercli-papers huggingface dataset:
Private Synthetic Text Generation with Diffusion Models
Authors: Sebastian Ochs; Ivan Habernal
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: How capable are diffusion models of generating synthetics texts? Recent research shows their strengths, with performance reaching that of auto-regressive LLMs. But are they also good in generating synthetic data if the training was under differential privacy? Here the evidence is missing, yet the promises from private image generation look strong. In this paper we address this open question by extensive experiments. At the same time, we critically assess (and reimplement) previous works on synthetic private text generation with LLMs and reveal some unmet assumptions that might have led to violating the differential privacy guarantees. Our results partly contradict previous non-private findings and show that fully open-source LLMs outperform diffusion models in the privacy regime. Our complete source codes, datasets, and experimental setup is publicly available to foster future research.
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling
Authors: Yiwen Ding; Zhiheng Xi; Wei He; Zhuoyuan Li; Yitao Zhai; Xiaowei Shi; Xunliang Cai; Tao Gui; Qi Zhang; Xuanjing Huang
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: Self-improvement methods enable large language models (LLMs) to generate solutions themselves and iteratively train on filtered, high-quality rationales. This process proves effective and reduces the reliance on human supervision in LLMs’ reasoning, but the performance soon plateaus. We delve into the process and find that models tend to over-sample on easy queries and under-sample on queries they have yet to master. As iterations proceed, this imbalance in sampling is exacerbated, leading to a long-tail distribution where solutions to difficult queries almost diminish. This phenomenon limits the performance gain of self-improving models. A straightforward solution is brute-force sampling to balance the distribution, which significantly raises computational costs. In this paper, we introduce Guided Self-Improvement (GSI), a strategy aimed at improving the efficiency of sampling challenging heavy-tailed data. It leverages Socratic-style guidance signals to help LLM reasoning with complex queries, reducing the exploration effort and minimizing computational overhead. Experiments on four models across diverse mathematical tasks show that GSI strikes a balance between performance and efficiency, while also being effective on held-out tasks.
FactEval: Evaluating the Robustness of Fact Verification Systems in the Era of Large Language Models
Authors: Mamta; Oana Cocarascu
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: Whilst large language models (LLMs) have made significant advances in every natural language processing task, studies have shown that these models are vulnerable to small perturbations in the inputs, raising concerns about their robustness in the real-world. Given the rise of misinformation online and its significant impact on society, fact verification is one area in which assessing the robustness of models developed for this task is crucial. However, the robustness of LLMs in fact verification remains largely unexplored. In this paper, we introduce FactEval, a novel large-scale benchmark for extensive evaluation of LLMs in the fact verification domain covering 17 realistic word-level and character-level perturbations and 4 types of subpopulations. We investigate the robustness of several LLMs in zero-shot, few-shot, and chain-of-thought prompting. Our analysis using FEVER, one of the largest and most widely-used datasets for fact verification, reveals that LLMs are brittle to small input changes and also exhibit performance variations across different subpopulations.
Analyzing Memorization in Large Language Models through the Lens of Model Attribution
Authors: Tarun Ram Menta; Susmit Agrawal; Chirag Agarwal
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: Large Language Models (LLMs) are prevalent in modern applications but often memorize training data, leading to privacy breaches and copyright issues. Existing research has mainly focused on post-hoc analyses—such as extracting memorized content or developing memorization metrics—without exploring the underlying architectural factors that contribute to memorization. In this work, we investigate memorization from an architectural lens by analyzing how attention modules at different layers impact its memorization and generalization performance. Using attribution techniques, we systematically intervene in the LLM’s architecture by bypassing attention modules at specific blocks while keeping other components like layer normalization and MLP transformations intact. We provide theorems analyzing our intervention mechanism from a mathematical view, bounding the difference in layer outputs with and without our attributions. Our theoretical and empirical analyses reveal that attention modules in deeper transformer blocks are primarily responsible for memorization, whereas earlier blocks are crucial for the model’s generalization and reasoning capabilities. We validate our findings through comprehensive experiments on different LLM families (Pythia and GPT-Neo) and five benchmark datasets. Our insights offer a practical approach to mitigate memorization in LLMs while preserving their performance, contributing to safer and more ethical deployment in real-world applications.
Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL
Authors: Bingfeng Chen; Shaobin Shi; Yongqi Luo; Boyan Xu; Ruichu Cai; Zhifeng Hao
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: Generative language models have shown significant potential in single-turn Text-to-SQL. However, their performance does not extend equivalently to multi-turn Text-to-SQL. This is primarily due to generative language models’ inadequacy in handling the complexities of context information and dynamic schema linking in multi-turn interactions. In this paper, we propose a framework named Track-SQL, which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. Specifically, Track-SQL incorporates a Semantic-enhanced Schema Extractor and a Schema-aware Context Extractor. Experimental results demonstrate that Track-SQL achieves state-of-the-art performance on the SparC and CoSQL datasets. Furthermore, detailed ablation studies reveal that Track-SQL significantly improves execution accuracy in multi-turn interactions by 7.1% and 9.55% on these datasets, respectively. Our implementation will be open-sourced at https://github.com/DMIRLAB-Group/Track-SQL.
Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss
Authors: Kunal Dahiya; Diego Ortego; David Jimenez-Cabello
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: Extreme Multi-label Classification (XMC) methods predict relevant labels for a given query in an extremely large label space. Recent works in XMC address this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. However, learning deep models can be computationally expensive in large output spaces, resulting in a trade-off between high performing brute-force approaches and efficient solutions. In this paper, we propose PRIME, a XMC method that employs a novel prototypical contrastive learning technique to reconcile efficiency and performance surpassing brute-force approaches. We frame XMC as a data-to-prototype prediction task where label prototypes aggregate information from related queries. More precisely, we use a shallow transformer encoder that we coin as Label Prototype Network, which enriches label representations by aggregating text-based embeddings, label centroids and learnable free vectors. We jointly train a deep encoder and the Label Prototype Network using an adaptive triplet loss objective that better adapts to the high granularity and ambiguity of extreme label spaces. PRIME achieves state-of-the-art results in several public benchmarks of different sizes and domains, while keeping the model efficient.
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback
Authors: Zonghai Yao; Aditya Parashar; Huixue Zhou; Won Seok Jang; Feiyun Ouyang; Zhichao Yang; Hong Yu
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: Automatic question generation (QG) is essential for AI and NLP, particularly in intelligent tutoring, dialogue systems, and fact verification. Generating multiple-choice questions (MCQG) for professional exams, like the United States Medical Licensing Examination (USMLE), is particularly challenging, requiring domain expertise and complex multi-hop reasoning for high-quality questions. However, current large language models (LLMs) like GPT-4 struggle with professional MCQG due to outdated knowledge, hallucination issues, and prompt sensitivity, resulting in unsatisfactory quality and difficulty. To address these challenges, we propose MCQG-SRefine, an LLM self-refine-based (Critique and Correction) framework for converting medical cases into high-quality USMLE-style questions. By integrating expert-driven prompt engineering with iterative self-critique and self-correction feedback, MCQG-SRefine significantly enhances human expert satisfaction regarding both the quality and difficulty of the questions. Furthermore, we introduce an LLM-as-Judge-based automatic metric to replace the complex and costly expert evaluation process, ensuring reliable and expert-aligned assessments.
Main Predicate and Their Arguments as Explanation Signals For Intent Classification
Authors: Sameer Pimparkhede; Pushpak Bhattacharyya
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: Intent classification is crucial for conversational agents (chatbots), and deep learning models perform well in this area. However, little research has been done on the explainability of intent classification due to the absence of suitable benchmark data. Human annotation of explanation signals in text samples is time-consuming and costly. However, from inspection of data on intent classification, we see that, more often than not, the main verb denotes the action, and the direct object indicates the domain of conversation, serving as explanation signals for intent. This observation enables us to hypothesize that the main predicate in the text utterances, along with the arguments of the main predicate, can serve as explanation signals. Leveraging this, we introduce a new technique to automatically augment text samples from intent classification datasets with word-level explanations. We mark main predicates (primarily verbs) and their arguments (dependency relations) as explanation signals in benchmark intent classification datasets ATIS and SNIPS, creating a unique 21k-instance dataset for explainability. Further, we experiment with deep learning and language models. We observe that models that work well for classification do not perform well in explainability metrics like plausibility and faithfulness. We also observe that guiding models to focus on explanation signals from our dataset during training improves the plausibility Token F1 score by 3-4%, improving the model’s reasoning.
Handling Missing Entities in Zero-Shot Named Entity Recognition: Integrated Recall and Retrieval Augmentation
Authors: Ruichu Cai; Junhao Lu; Zhongjie Chen; Boyan Xu; Zhifeng Hao
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: Zero-shot Named Entity Recognition (ZS-NER) aims to recognize entities in unseen domains without specific annotated data. A key challenge is handling missing entities while ensuring accurate type recognition, hindered by: 1) the pre-training assumption that each entity has a single type, overlooking diversity, and 2) insufficient contextual knowledge for type reasoning. To address this, we propose IRRA (Integrated Recall and Retrieval Augmentation), a novel two-stage framework leveraging large language model techniques. In the Recall Augmented Entity Extracting stage, we built a perturbed dataset to induce the model to exhibit missing or erroneous extracted entities. Based on this, we trained an enhanced model to correct these errors. This approach can improve the ZS-NER’s recall rate. In the Retrieval Augmented Type Correcting stage, we employ Retrieval-Augmented Generation techniques to locate entity-related unannotated contexts, with the additional contextual information significantly improving the accuracy of type correcting. Extensive evaluations demonstrate the state-of-the-art performance of our IRRA, with significant improvements in zero-shot cross-domain settings validated through both auto-evaluated metrics and analysis. Our implementation will be open-sourced athttps://github.com/DMIRLAB-Group/IRRA.
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy
Authors: Hyunjong Kim; Suyeon Lee; Yeongjae Cho; Eunseo Ryu; Yohan Jo; Suran Seong; Sungzoon Cho
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: The increasing demand for mental health services has led to the rise of AI-driven mental health chatbots, though challenges related to privacy, data collection, and expertise persist. Motivational Interviewing (MI) is gaining attention as a theoretical basis for boosting expertise in the development of these chatbots. However, existing datasets are showing limitations for training chatbots, leading to a substantial demand for publicly available resources in the field of MI and psychotherapy. These challenges are even more pronounced in non-English languages, where they receive less attention. In this paper, we propose a novel framework that simulates MI sessions enriched with the expertise of professional therapists. We train an MI forecaster model that mimics the behavioral choices of professional therapists and employ Large Language Models (LLMs) to generate utterances through prompt engineering. Then, we present KMI, the first synthetic dataset theoretically grounded in MI, containing 1,000 high-quality Korean Motivational Interviewing dialogues. Through an extensive expert evaluation of the generated dataset and the dialogue model trained on it, we demonstrate the quality, expertise, and practicality of KMI. We also introduce novel metrics derived from MI theory in order to evaluate dialogues from the perspective of MI.
Automatic Input Rewriting Improves Translation with Large Language Models
Authors: Dayeon Ki; Marine Carpuat
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: Can we improve machine translation (MT) with LLMs by rewriting their inputs automatically? Users commonly rely on the intuition that well-written text is easier to translate when using off-the-shelf MT systems. LLMs can rewrite text in many ways but in the context of MT, these capabilities have been primarily exploited to rewrite outputs via post-editing. We present an empirical study of 21 input rewriting methods with 3 open-weight LLMs for translating from English into 6 target languages. We show that text simplification is the most effective MT-agnostic rewrite strategy and that it can be improved further when using quality estimation to assess translatability. Human evaluation further confirms that simplified rewrites and their MT outputs both largely preserve the original meaning of the source and MT. These results suggest LLM-assisted input rewriting as a promising direction for improving translations.
HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
Authors: Vladimir Malinovskii; Andrei Panferov; Ivan Ilin; Han Guo; Peter Richtárik; Dan Alistarh
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: Quantizing large language models has become a standard way to reduce their memory and computational costs. Typically, existing methods focus on breaking down the problem into individual layer-wise sub-problems, and minimizing per-layer error, measured via various metrics. Yet, this approach currently lacks theoretical justification and the metrics employed may be sub-optimal. In this paper, we present a “linearity theorem” establishing a direct relationship between the layer-wise reconstruction error and the model perplexity increase due to quantization. This insight enables two novel applications: (1) a simple data-free LLM quantization method using Hadamard rotations and MSE-optimal grids, dubbed HIGGS, which outperforms all prior data-free approaches such as the extremely popular NF4 quantized format, and (2) an optimal solution to the problem of finding non-uniform per-layer quantization levels which match a given compression constraint, obtained by reduction to dynamic programming. On the practical side, we demonstrate improved accuracy-compression trade-offs on Llama-family models, advancing both data-free and non-uniform quantization for large language models.
The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Units
Authors: Badr AlKhamissi; Greta Tuckute; Antoine Bosselut; Martin Schrimpf
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: Large language models (LLMs) exhibit remarkable capabilities on not just language tasks, but also various tasks that are not linguistic in nature, such as logical reasoning and social inference. In the human brain, neuroscience has identified a core language system that selectively and causally supports language processing. We here ask whether similar specialization for language emerges in LLMs. We identify language-selective units within 18 popular LLMs, using the same localization approach that is used in neuroscience. We then establish the causal role of these units by demonstrating that ablating LLM language-selective units – but not random units – leads to drastic deficits in language tasks. Correspondingly, language-selective LLM units are more aligned to brain recordings from the human language system than random units. Finally, we investigate whether our localization method extends to other cognitive domains: while we find specialized networks in some LLMs for reasoning and social capabilities, there are substantial differences among models. These findings provide functional and causal evidence for specialization in large language models, and highlight parallels with the functional organization in the brain.
MixLLM: Dynamic Routing in Mixed Large Language Models
Authors: Xinyuan Wang; Yanchi Liu; Wei Cheng; Xujiang Zhao; Zhengzhang Chen; Wenchao Yu; Yanjie Fu; Haifeng Chen
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: Large Language Models (LLMs) exhibit potential artificial generic intelligence recently, however, their usage is costly with high response latency. Given mixed LLMs with their own strengths and weaknesses, LLM routing aims to identify the most suitable model for each query in the stream to maximize response quality and minimize cost and latency. However, the challenges involve: (1) dynamic trade-offs among quality, cost, and latency; (2) enabling continual learning in deployed systems; and (3) navigating a varying (e.g., new LLM addition or old LLM removal) set of LLM candidates over time. To bridge these gaps, we develop MixLLM, a dynamic contextual-bandit-based routing system for query-LLM assignment. Specifically, we first leverage query tags to enhance query embeddings for the routing task. Next, we design lightweight prediction models to estimate the response qualities and costs of queries over LLMs. We then devise a meta-decision maker to choose the query-LLM assignments to best tradeoff response quality, cost, and latency. Finally, the system benefits from continual training, allowing it to adapt to evolving queries and user feedback over time. Our extensive experiments show that MixLLM achieves the best trade-offs in response quality, cost, and latency (97.25% of GPT-4’s quality at 24.18% of the cost under the time constraint).
Continual Learning in Multilingual Sign Language Translation
Authors: Shakib Yazdani; Josef Van Genabith; Cristina España-Bonet
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: The field of sign language translation (SLT) is still in its infancy, as evidenced by the low translation quality, even when using deep learn- ing approaches. Probably because of this, many common approaches in other machine learning fields have not been explored in sign language. Here, we focus on continual learning for mul- tilingual SLT. We experiment with three con- tinual learning methods and compare them to four more naive baseline and fine-tuning ap- proaches. We work with four sign languages (ASL, BSL, CSL and DGS) and three spo- ken languages (Chinese, English and German). Our results show that incremental fine-tuning is the best performing approach both in terms of translation quality and transfer capabilities, and that continual learning approaches are not yet fully competitive given the current SOTA in SLT.