GenAI4ELab/papercli-papers huggingface dataset:
Acoustic Individual Identification of White-Faced Capuchin Monkeys Using Joint Multi-Species Embeddings
Authors: Álvaro Vega-Hidalgo; Artem Abzaliev; Thore Bergman; Rada Mihalcea
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Acoustic individual identification of wild animals is an essential task for understanding animal vocalizations within their social contexts, and for facilitating conservation and wildlife monitoring efforts. However, most of the work in this space relies on human efforts, as the development of methods for automatic individual identification is hindered by the lack of data. In this paper, we explore cross-species pre-training to address the task of individual classification in white-faced capuchin monkeys. Using acoustic embeddings from birds and humans, we find that they can be effectively used to identify the calls from individual monkeys. Moreover, we find that joint multi-species representations can lead to further improvements over the use of one representation at a time. Our work demonstrates the potential of cross-species data transfer and multi-species representations, as strategies to address tasks on species with very limited data.
SELF-PERCEPT: Introspection Improves Large Language Models’ Detection of Multi-Person Mental Manipulation in Conversations
Authors: Danush Khanna; Pratinav Seth; Sidhaarth Sredharan Murali; Aditya Kumar Guru; Siddharth Shukla; Tanuj Tyagi; Sandeep Chaurasia; Kripabandhu Ghosh
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipulation’s nuanced and context-specific nature, identifying manipulative language in complex, multi-turn, and multi-person conversations remains a significant challenge for large language models (LLMs). To address this gap, we introduce the MultiManip dataset, comprising 220 multi-turn, multi-person dialogues balanced between manipulative and non-manipulative interactions, all drawn from reality shows that mimic real-world scenarios. For manipulative interactions, it includes 11 distinct manipulations depicting real-life scenarios. We conduct extensive evaluations of state-of-the-art LLMs, such as GPT-4o and Llama-3.1-8B, employing various prompting strategies. Despite their capabilities, these models often struggle to detect manipulation effectively. To overcome this limitation, we propose SELF-PERCEPT, a novel, two-stage prompting framework inspired by Self-Perception Theory, demonstrating strong performance in detecting multi-person, multi-turn mental manipulation. Our code and data are publicly available at https://github.com/danushkhanna/self-percept .
A Variational Approach for Mitigating Entity Bias in Relation Extraction
Authors: Samuel Mensah; Elena Kochkina; Jabez Magomere; Joy Prakash Sain; Simerjot Kaur; Charese Smiley
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Mitigating entity bias is a critical challenge in Relation Extraction (RE), where models often rely excessively on entities, resulting in poor generalization. This paper presents a novel approach to address this issue by adapting a Variational Information Bottleneck (VIB) framework. Our method compresses entity-specific information while preserving task-relevant features. It achieves state-of-the-art performance on both general and financial domain RE datasets, excelling in in-domain settings (original test sets) and out-of-domain (modified test sets with type-constrained entity replacements). Our approach offers a robust, interpretable, and theoretically grounded methodology.
GenKnowSub: Improving Modularity and Reusability of LLMs through General Knowledge Subtraction
Authors: Mohammadtaha Bagherifard; Sahar Rajabi; Ali Edalat; Yadollah Yaghoobzadeh
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Large language models (LLMs) often struggle with zero-shot generalization, and several modular approaches have been proposed to address this challenge. Yet, we hypothesize that a key limitation remains: the entanglement of general knowledge and task-specific adaptations. To overcome this, we propose a modular framework that disentangles these components by constructing a library of task-specific LoRA modules alongside a general-domain LoRA. By subtracting this general knowledge component from each task-specific module, we obtain residual modules that focus more exclusively on task-relevant information. We call this approach general knowledge subtraction or GenKnowSub. Leveraging the refined task-specific modules and the Arrow routing algorithm, we dynamically select and combine modules for new inputs without additional training. Our studies on the Phi-3 model and standard Arrow as baselines reveal that using general knowledge LoRAs derived from diverse languages, including English, French, and German, yields consistent performance gains in both monolingual and cross-lingual settings across a wide set of benchmarks. Further experiments on Phi-2 reveal how GenKnowSub generalizes to a weaker LLM.
The Role of Abstract Representations and Observed Preferences in the Ordering of Binomials in Large Language Models
Authors: Zachary Nicholas Houghton; Kenji Sagae; Emily Morgan
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: To what extent do large language models learn abstract representations as opposed to more superficial aspects of their very large training corpora? We examine this question in the context of binomial ordering preferences involving two conjoined nouns in English. When choosing a binomial ordering (radio and television vs television and radio), humans rely on more than simply the observed frequency of each option. Humans also rely on abstract ordering preferences (e.g., preferences for short words before long words). We investigate whether large language models simply rely on the observed preference in their training data, or whether they are capable of learning the abstract ordering preferences (i.e., abstract representations) that humans rely on. Our results suggest that both smaller and larger models’ ordering preferences are driven exclusively by their experience with that item in the training data. Our study provides further insights into differences between how large language models represent and use language and how humans do it, particularly with respect to the use of abstract representations versus observed preferences.
Can LLMs Understand Unvoiced Speech? Exploring EMG-to-Text Conversion with LLMs
Authors: Payal Mohapatra; Akash Pandey; Xiaoyuan Zhang; Qi Zhu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Unvoiced electromyography (EMG) is an effective communication tool for individuals unable to produce vocal speech. However, most prior methods rely on paired voiced and unvoiced EMG signals, along with speech data, for unvoiced EMG-to-text conversion, which is not practical for these individuals. Given the rise of large language models (LLMs) in speech recognition, we explore their potential to understand unvoiced speech. To this end, we address the challenge of learning from unvoiced EMG alone and propose a novel EMG adaptor module that maps EMG features to an LLM’s input space, achieving an average word error rate of 0.49 on a closed-vocabulary unvoiced EMG-to-text task. Even with a conservative data availability of just six minutes, our approach improves performance over specialized models by nearly 20%. While LLMs have been shown to be extendable to new language modalities—such as audio—understanding articulatory biosignals, like unvoiced EMG, is more challenging. This work takes a crucial first step toward enabling LLMs to comprehend unvoiced speech using surface EMG.
Decoder-Only LLMs can be Masked Auto-Encoders
Authors: Dan Qiao; Yuan Gao; Zheming Yang; Di Yang; Ziheng Wu; Pengcheng Lu; Minghui Qiu; Juntao Li; Min Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Modern NLP workflows (e.g., RAG systems) require different models for generation and embedding tasks, where bidirectional pre-trained encoders and decoder-only Large Language Models (LLMs) dominate respective tasks. Structural differences between models result in extra development costs and limit knowledge sharing between tasks. In this work, we present UniMAE, a novel unsupervised training method that transforms an Decoder-Only LLM into a Uni-Directional Masked Auto-Encoder. UniMAE compresses high-quality semantic information into the [EOS] embedding while preserving the generation capabilities of LLMs. Comprehensive evaluations across 56 MTEB datasets demonstrate that UniMAE can achieve state-of-the-art results under unsupervised settings with merely 100 training steps, establishing the first effective approach to unifying generation and representation learning in decoder-only architectures.
Mitigating Posterior Salience Attenuation in Long-Context LLMs with Positional Contrastive Decoding
Authors: Zikai Xiao; Ziyang Wang; Wen Ma; Yan Zhang; Wei Shen; Yan Wang; Luqi Gong; Zuozhu Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: While Large Language Models (LLMs) support long contexts, they struggle with performance degradation within the context window. Current solutions incur prohibitive training costs, leaving statistical behaviors and cost-effective approaches underexplored. From the decoding perspective, we identify the Posterior Salience Attenuation (PSA) phenomenon, where the salience ratio correlates with long-text performance degradation. Notably, despite the attenuation, gold tokens still occupy high-ranking positions in the decoding space. Motivated by it, we propose the training-free Positional Contrastive Decoding (PCD) that contrasts the logits derived from long-aware attention with those from designed local-aware attention, enabling the model to focus on the gains introduced by large-scale short-to-long training. Through the analysis of long-term decay simulation, we demonstrate that PCD effectively alleviates attention score degradation. Experimental results show that PCD achieves state-of-the-art performance on long-context benchmarks.
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs
Authors: Xuan Zhang; Cunxiao Du; Sicheng Yu; Jiawei Wu; Fengzhuo Zhang; Wei Gao; Qian Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Due to the auto-regressive nature of current video large language models (Video-LLMs), the inference latency increases as the input sequence length grows, posing challenges for the efficient processing of video sequences that are usually very long. We observe that during decoding, the attention scores of most tokens in Video-LLMs tend to be sparse and concentrated, with only certain tokens requiring comprehensive full attention. Based on this insight, we introduce Sparse-to-Dense (StD), a novel decoding strategy that integrates two distinct modules: one leveraging sparse top-K attention and the other employing dense full attention. These modules collaborate to accelerate Video-LLMs without loss. The fast (sparse) model speculatively decodes multiple tokens, while the slow (dense) model verifies them in parallel. StD is a tuning-free, plug-and-play solution that achieves up to a 1.94 walltime speedup in video processing. It maintains model performance while enabling a seamless transition from a standard Video-LLM to a sparse Video-LLM with minimal code modifications.
Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
Authors: Andrea Santilli; Adam Golinski; Michael Kirchhof; Federico Danieli; Arno Blaas; Miao Xiong; Luca Zappella; Sinead Williamson
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Uncertainty Quantification (UQ) in Language Models (LMs) is key to improving their safety and reliability. Evaluations often use metrics like AUROC to assess how well UQ methods (e.g., negative sequence probabilities) correlate with task correctness functions (e.g., ROUGE-L). We show that mutual biases-when both UQ methods and correctness functions are biased by the same factors-systematically distort evaluation. First, we formally prove that any mutual bias non-randomly skews AUROC rankings, compromising benchmark integrity. Second, we confirm this happens empirically by testing 7 widely used correctness functions, from lexical-based and embedding-based metrics to LM-as-a-judge approaches, across 4 datasets × 4 models × 8 UQ methods. Our analysis showsthat length biases in correctness functions distort UQ assessments by interacting with length biases in UQ methods. We identify LM-as-a-judge methods as the least length-biased, offering a promising path for a fairer UQ evaluation.
Memorization Inheritance in Sequence-Level Knowledge Distillation for Neural Machine Translation
Authors: Verna Dankers; Vikas Raunak
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: In this work, we explore how instance-level memorization in the teacher Neural Machine Translation (NMT) model gets inherited by the student model in sequence-level knowledge distillation (SeqKD). We find that despite not directly seeing the original training data, students memorize more than baseline models (models of the same size, trained on the original data)—3.4% for exact matches and 57% for extractive memorization—and show increased hallucination rates. Further, under this SeqKD setting, we also characterize how students behave on specific training data subgroups, such as subgroups with low quality or specific counterfactual memorization (CM) scores, and find that students exhibit greater denoising on low-quality subgroups. Finally, we propose a modification to SeqKD named Adaptive-SeqKD, which intervenes in SeqKD to reduce memorization and hallucinations. Overall, we recommend caution when applying SeqKD: students inherit both their teachers’ superior performance and their fault modes, thereby requiring active monitoring.
CoRet: Improved Retriever for Code Editing
Authors: Fabio James Fehr; Prabhu Teja S; Luca Franceschi; Giovanni Zappella
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: In this paper, we introduce CoRet, a dense retrieval model designed for code-editing tasks that integrates code semantics, repository structure, and call-graph dependencies. The model focuses on retrieving relevant portions of a code repository based on natural language queries such as requests to implement new features or fix bugs. These retrieved code chunks can then be presented to an user or to a second code-editing model or agent. To train CoRet, we propose a loss function explicitly designed for repository-level retrieval. On SWE-bench and Long Code Arena’s bug localisation datasets, we show that our model substantially improves retrieval recall by at least 15 percentage points over existing models, and ablate the design choices to show their importance in achieving these results.
Has Machine Translation Evaluation Achieved Human Parity? The Human Reference and the Limits of Progress
Authors: Lorenzo Proietti; Stefano Perrella; Roberto Navigli
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: In Machine Translation (MT) evaluation, metric performance is assessed based on agreement with human judgments. In recent years, automatic metrics have demonstrated increasingly high levels of agreement with humans. To gain a clearer understanding of metric performance and establish an upper bound, we incorporate human baselines in the MT meta-evaluation, that is, the assessment of MT metrics’ capabilities. Our results show that human annotators are not consistently superior to automatic metrics, with state-of-the-art metrics often ranking on par with or higher than human baselines. Despite these findings suggesting human parity, we discuss several reasons for caution. Finally, we explore the broader implications of our results for the research field, asking: Can we still reliably measure improvements in MT evaluation? With this work, we aim to shed light on the limits of our ability to measure progress in the field, fostering discussion on an issue that we believe is crucial to the entire MT evaluation community.
Diffusion Directed Acyclic Transformer for Non-Autoregressive Machine Translation
Authors: Quan Nguyen-Tri; Cong Dao Tran; Hoang Thanh-Tung
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Non-autoregressive transformers (NATs) predict entire sequences in parallel to reduce decoding latency, but they often encounter performance challenges due to the multi-modality problem. A recent advancement, the Directed Acyclic Transformer (DAT), addresses this issue by capturing multiple translation modalities to paths in a Directed Acyclic Graph (DAG). However, the collaboration with the latent variable introduced through the Glancing training (GLAT) is crucial for DAT to attain state-of-the-art performance. In this paper, we introduce Diffusion Directed Acyclic Transformer (Diff-DAT), which serves as an alternative to GLAT as a latent variable introduction for DAT. Diff-DAT offers two significant benefits over the previous approach. Firstly, it establishes a stronger alignment between training and inference. Secondly, it facilitates a more flexible tradeoff between quality and latency.
Efficient Knowledge Editing via Minimal Precomputation
Authors: Akshat Gupta; Maochuan Lu; Thomas Hartvigsen; Gopala Anumanchipalli
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Knowledge editing methods like MEMIT are able to make data and compute efficient updates of factual knowledge by using a single sentence to update facts and their consequences. However, what is often overlooked is a “precomputation step”, which requires a one-time but significant computational cost. The authors of MEMIT (CITATION) originally precompute approximately 44 million hidden vectors per edited layer, which requires a forward pass over 44 million tokens. For GPT-J (6B), this precomputation step takes 36 hours on a single GPU, while it takes approximately 40 hours for Llama2-7B. Additionally, this precomputation time grows with model size. In this paper, we show that this excessive computational cost is unnecessary. Knowledge editing using MEMIT and related methods, such as ROME and EMMET, can be performed by pre-computing a very small portion of the 44 million hidden vectors. We first present the theoretical minimum number of hidden vector precomputation required for solutions of these editing methods to exist. We then empirically show that knowledge editing using these methods can be done by pre-computing significantly fewer hidden vectors. Specifically, we show that the precomputation step can be done with less than 0.3% of the originally stipulated number of hidden vectors. This saves a significant amount of precomputation time and allows users to begin editing new models within a few minutes.
Meaning Variation and Data Quality in the Corpus of Founding Era American English
Authors: Dallas Card
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: Legal scholars are increasingly using corpus based methods for assessing historical meaning. Among work focused on the so-called founding era (mid to late 18th century), the majority of such studies use the Corpus of Founding Era American English (COFEA) and rely on methods such as word counting and manual coding. Here, we demonstrate what can be inferred about meaning change and variation using more advanced NLP methods, focusing on terms in the U.S. Constitution. We also carry out a data quality assessment of COFEA, pointing out issues with OCR quality and metadata, compare diachronic change to synchronic variation, and discuss limitations when using NLP methods for studying historical meaning.
MindRef: Mimicking Human Memory for Hierarchical Reference Retrieval with Fine-Grained Location Awareness
Authors: Ye Wang; Xinrun Xu; Zhiming Ding
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: When completing knowledge-intensive tasks, humans sometimes need an answer and a corresponding reference passage for auxiliary reading. Previous methods required obtaining pre-segmented article chunks through additional retrieval models. This paper explores leveraging the parameterized knowledge stored during the pre-training phase of large language models (LLMs) to recall reference passage from any starting position independently. We propose a two-stage framework that simulates the scenario of humans recalling easily forgotten references. Initially, the LLM is prompted to recall document title identifiers to obtain a coarse-grained document set. Then, based on the acquired coarse-grained document set, it recalls fine-grained passage. In the two-stage recall process, we use constrained decoding to ensure that content outside of the stored documents is not generated. To increase speed, we only recall a short prefix in the second stage, and then locate its position to retrieve a complete passage. Experiments on KILT knowledge-sensitive tasks have verified that LLMs can independently recall reference passage locations in various task forms, and the obtained reference significantly assists downstream tasks.
LLMs syntactically adapt their language use to their conversational partner
Authors: Florian Kandra; Vera Demberg; Alexander Koller
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: It has been frequently observed that human speakers align their language use with each other during conversations. In this paper, we study empirically whether large language models (LLMs) exhibit the same behavior of conversational adaptation.We construct a corpus of conversations between LLMs and find that two LLM agents end up making more similar syntactic choices as conversations go on, confirming that modern LLMs adapt their language use to their conversational partners in at least a rudimentary way.
TigerLLM - A Family of Bangla Large Language Models
Authors: Nishat Raihan; Marcos Zampieri
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers))

Abstract: The development of Large Language Models (LLMs) remains heavily skewed towards English and a few other high-resource languages. This linguistic disparity is particularly evident for Bangla - the 5th most spoken language. A few initiatives attempted to create open-source Bangla LLMs with performance still behind high-resource languages and limited reproducibility. To address this gap, we introduce TigerLLM - a family of Bangla LLMs. Our results demonstrate that these models surpass all open-source alternatives and also outperform larger proprietary models like GPT3.5 across standard benchmarks, establishing TigerLLM as the new baseline for future Bangla language modeling.