GenAI4ELab/papercli-papers huggingface dataset:
Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection
Authors: Jiatao Li; Xiaojun Wan
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The rise of Large Language Models (LLMs) necessitates accurate AI-generated text detection. However, current approaches largely overlook the influence of author characteristics. We investigate how sociolinguistic attributes—gender, CEFR proficiency, academic field, and language environment—impact state-of-the-art AI text detectors. Using the ICNALE corpus of human-authored texts and parallel AI-generated texts from diverse LLMs, we conduct a rigorous evaluation employing multi-factor ANOVA and weighted least squares (WLS). Our results reveal significant biases: CEFR proficiency and language environment consistently affected detector accuracy, while gender and academic field showed detector-dependent effects. These findings highlight the crucial need for socially aware AI text detection to avoid unfairly penalizing specific demographic groups. We offer novel empirical evidence, a robust statistical framework, and actionable insights for developing more equitable and reliable detection systems in real-world, out-of-domain contexts. This work paves the way for future research on bias mitigation, inclusive evaluation benchmarks, and socially responsible LLM detectors.
RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates
Authors: Md Kowsher; Tara Esmaeilbeig; Chun-Nam Yu; Chen Chen; Mojtaba Soltanalian; Niloofar Yousefi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: We propose Row-Column Fine-Tuning(RoCoFT), a parameter-efficient fine-tuning method for large language models based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-sized LMs like RoBERTa and DeBERTa, and larger LMs like Bloom-7B, Llama2-7B, and Llama2-13B, we show that our method gives comparable or better accuracies than state-of-the-art Parameter-Efficient Finetuning methods while also being more memory and computation-efficient. We also study the reason behind the effectiveness of our method with tools from neural tangent kernel theory. We empirically demonstrate that our kernel, constructed using a restricted set of row and column parameters, is numerically close to the full-parameter kernel and gives comparable classification performance. Ablation studies are conducted to investigate the impact of different algorithmic choices, including the robustness of RoCoFT to any selection of rows and columns, as well as the optimal rank for the effective implementation of our method.
Scaling Laws and Efficient Inference for Ternary Language Models
Authors: Tejas Vaidhya; Ayush Kaushal; Vineet Jain; Francis Couture-Harpin; Prashant Shishodia; Majid Behbahani; Yuriy Nevmyvaka; Irina Rish
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Large language models (LLMs) are increasingly used across research and industry applications, yet their inference efficiency remains a significant challenge. As the computational power of modern GPU architectures continuously improves, their memory bandwidth and capacity have not scaled proportionally, creating a critical bottleneck during inference. To address this, we investigate ternary language models (TriLMs) that employ quantization-aware training to significantly reduce memory requirements. We first analyze the scalability of TriLMs by conducting a scaling law analysis, revealing that TriLMs benefit more from increasing training data than from scaling model parameters. Based on this observation, we introduce TriTera, an open suite of TriLMs trained on up to 1.2 trillion tokens, demonstrating sustained performance gains at scale. Furthermore, to improve inference efficiency, we propose novel 2-bit and 1.6-bit packing schemes for ternary weights, which demonstrate accelerated inference across various CPU architectures. Building on the 2-bit packing, we develop a GPU kernel called TriRun that accelerates end-to-end model inference by up to 5 × compared to floating-point baselines. To encourage further exploration and development of TriLMs, we will release the TriTera suite and TriRun inference kernels. Overall, our work lays the foundation for building and deploying efficient LLMs, providing a valuable resource for the research community.
Exploring the Impact of Instruction-Tuning on LLM’s Susceptibility to Misinformation
Authors: Kyubeen Han; Junseo Jang; Hongjin Kim; Geunyeong Jeong; Harksoo Kim
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Instruction-tuning enhances the ability of large language models (LLMs) to follow user instructions more accurately, improving usability while reducing harmful outputs. However, this process may increase the model’s dependence on user input, potentially leading to the unfiltered acceptance of misinformation and the generation of hallucinations. Existing studies primarily highlight that LLMs are receptive to external information that contradict their parametric knowledge, but little research has been conducted on the direct impact of instruction-tuning on this phenomenon. In our study, we investigate the impact of instruction-tuning on LLM susceptibility to misinformation. Our analysis reveals that instruction-tuned LLMs are significantly more likely to accept misinformation when it is presented by the user. A comparison with base models shows that instruction-tuning increases reliance on user-provided information, shifting susceptibility from the assistant role to the user role. Furthermore, we explore additional factors influencing misinformation susceptibility, such as the role of the user in prompt structure, misinformation length, and the presence of warnings in the system prompt. Our findings underscore the need for systematic approaches to mitigate unintended consequences of instruction-tuning and enhance the reliability of LLMs in real-world applications.
Do Language Models Understand Honorific Systems in Javanese?
Authors: Mohammad Rifqi Farhansyah; Iwan Darmawan; Adryan Kusumawardhana; Genta Indra Winata; Alham Fikri Aji; Derry Tanti Wijaya
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The Javanese language features a complex system of honorifics that vary according to the social status of the speaker, listener, and referent. Despite its cultural and linguistic significance, there has been limited progress in developing a comprehensive corpus to capture these variations for natural language processing (NLP) tasks. In this paper, we present Unggah-Ungguh, a carefully curated dataset designed to encapsulate the nuances of Unggah-Ungguh Basa, the Javanese speech etiquette framework that dictates the choice of words and phrases based on social hierarchy and context. Using Unggah-Ungguh, we assess the ability of language models (LMs) to process various levels of Javanese honorifics through classification and machine translation tasks. To further evaluate cross-lingual LMs, we conduct machine translation experiments between Javanese (at specific honorific levels) and Indonesian. Additionally, we explore whether LMs can generate contextually appropriate Javanese honorifics in conversation tasks, where the honorific usage should align with the social role and contextual cues. Our findings indicate that current LMs struggle with most honorific levels, exhibiting a bias toward certain honorific tiers.
Generative Reward Modeling via Synthetic Criteria Preference Learning
Authors: Xiaobo Liang; Haoke Zhang; Juntao Li; Kehai Chen; Qiaoming Zhu; Min Zhang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Generative Reward Models (GenRMs) leverage synthesized Chains of Thought (CoT) to reduce the need for massive labeled data, but this approach introduces risks of overoptimization due to the inability to guarantee the correctness of the CoTs. Identifying and optimizing unexpected behaviors within these synthesized CoT remains a challenge, as it heavily depends on precise annotations of intermediate behavior, similar to process supervision. In this work, we introduce a criteria-based preference tree for reward modeling, where each path in the tree represents a reasoning trajectory based on synthesized criteria. Crucially, each reasoning trajectory can be independently optimized through RL algorithm. These fine-grained process reward signals are derived from the inference-time computations and predefined rules, eliminating the need for human supervision. In experiments, SyncPL showed significant improvements over baselines on multiple human preference benchmarks. We further demonstrate that synthesized data can be learned using a long CoT format, analogous to an o1-like model, further enhancing performance while keeping stability and efficiency during training.
Exploring Multimodal Relation Extraction of Hierarchical Tabular Data with Multi-task Learning
Authors: Xinyu Zhang; Aibo Song; Jingyi Qiu; Jiahui Jin; Tianbo Zhang; Xiaolin Fang
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Relation Extraction (RE) is a key task in table understanding, aiming to extract semantic relations between columns. However, complex tables with hierarchical headers are hard to obtain high-quality textual formats (e.g., Markdown) for input under practical scenarios like webpage screenshots and scanned documents, while table images are more accessible and intuitive. Besides, existing works overlook the need of mining relations among multiple columns rather than just the semantic relation between two specific columns in real-world practice. In this work, we explore utilizing Multimodal Large Language Models (MLLMs) to address RE in tables with complex structures. We creatively extend the concept of RE to include calculational relations, enabling multi-task learning of both semantic and calculational RE for mutual reinforcement. Specifically, we reconstruct table images into graph structure based on neighboring nodes to extract graph-level visual features. Such feature enhancement alleviates the insensitivity of MLLMs to the positional information within table images. We then propose a Chain-of-Thought distillation framework with self-correction mechanism to enhance MLLMs’ reasoning capabilities without increasing parameter scale. Our method significantly outperforms most baselines on wide datasets. Additionally, we release a benchmark dataset for calculational RE in complex tables.
A Self-Denoising Model for Robust Few-Shot Relation Extraction
Authors: Liang Zhang; Yang Zhang; Ziyao Lu; Fandong Meng; Jie Zhou; Jinsong Su
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The few-shot relation extraction (FSRE) aims at enhancing the model’s generalization to new relations with very few labeled instances (support instances). Most existing studies use prototype networks (ProtoNets) for FSRE and assume that the support set, adapting the model to new relations, only contains accurately labeled instances. However, this assumption is usually unrealistic, as even carefully-annotated datasets often contain mislabeled instances. Thus, it is essential to enhance the robustness of FSRE models to noisy labels in support set, but this issue remains unexplored. In this paper, we first conduct a preliminary study, revealing the high sensitivity of ProtoNets to such noisy labels. Meanwhile, we discover that fully leveraging mislabeled support instances is crucial for enhancing the model’s robustness. To do this, we propose a self-denoising model for FSRE, which can automatically correct noisy labels of support instances. Specifically, our model comprises two core components: 1) a label correction module (LCM), used to correct mislabeled support instances based on the distances between them in the embedding space, and 2) a relation classification module (RCM), designed to achieve more robust relation prediction using the corrected labels generated by the LCM. Moreover, we propose a feedback-based training strategy, which focuses on training LCM and RCM to synergistically handle noisy labels in support set. Experimental results on two public datasets show the effectiveness and robustness of our model. Notably, even in scenarios without noisy labels, our model significantly outperforms all competitive baselines.
QuASAR: A Question-Driven Structure-Aware Approach for Table-to-Text Generation
Authors: WeiJie Liu; Yibin Zheng; Fang Kong
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Table-to-text generation aims to automatically produce natural language descriptions from structured or semi-structured tabular data. Unlike traditional text generation tasks, it requires models to accurately understand and represent table structures. Existing approaches typically process tables by linearizing them or converting them into graph structures. However, these methods either fail to adequately capture the table structure or rely on complex attention mechanisms, limiting their applicability. To tackle these challenges, we propose QuASAR, a question-driven self-supervised approach designed to enhance the model’s structural perception and representation capabilities. Specifically, QuASAR formulates a set of structure-related queries for self-supervised training, explicitly guiding the model to capture both local and global table structures. Additionally, we introduce two auxiliary pre-training tasks: a word-to-sentence reconstruction task and a numerical summarization task, which further enhance the fluency and factuality of the generated text. Experimental results on the ToTTo and HiTab datasets demonstrate that our approach produces higher-quality text compared to existing methods.
Automated Structured Radiology Report Generation
Authors: Jean-Benoit Delbrouck; Justin Xu; Johannes Moll; Alois Thomas; Zhihong Chen; Sophie Ostmeier; Asfandyar Azhar; Kelvin Zhenghao Li; Andrew Johnston; Christian Bluethgen; Eduardo Pontes Reis; Mohamed S Muneer; Maya Varma; Curtis Langlotz
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Automated radiology report generation from chest X-ray (CXR) images has the potential to improve clinical efficiency and reduce radiologists’ workload. However, most datasets, including the publicly available MIMIC-CXR and CheXpert Plus, consist entirely of free-form reports, which are inherently variable and unstructured. This variability poses challenges for both generation and evaluation: existing models struggle to produce consistent, clinically meaningful reports, and standard evaluation metrics fail to capture the nuances of radiological interpretation. To address this, we introduce Structured Radiology Report Generation (SRRG), a new task that reformulates free-text radiology reports into a standardized format, ensuring clarity, consistency, and structured clinical reporting. We create a novel dataset by restructuring reports using large language models (LLMs) following strict structured reporting desiderata. Additionally, we introduce SRR-BERT, a fine-grained disease classification model trained on 55 labels, enabling more precise and clinically informed evaluation of structured reports. To assess report quality, we propose F1-SRR-BERT, a metric that leverages SRR-BERT’s hierarchical disease taxonomy to bridge the gap between free-text variability and structured clinical reporting. We validate our dataset through a reader study conducted by five board-certified radiologists and extensive benchmarking experiments.
LPOI: Listwise Preference Optimization for Vision Language Models
Authors: Fatemeh Pesaran Zadeh; Yoojin Oh; Gunhee Kim
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Aligning large VLMs with human preferences is a challenging task, as methods like RLHF and DPO often overfit to textual information or exacerbate hallucinations. Although augmenting negative image samples partially addresses these pitfalls, no prior work has employed listwise preference optimization for VLMs, due to the complexity and cost of constructing listwise image samples. In this work, we propose LPOI, the first object-aware listwise preference optimization developed for reducing hallucinations in VLMs. LPOI identifies and masks a critical object in the image, and then interpolates the masked region between the positive and negative images to form a sequence of incrementally more complete images. The model is trained to rank these images in ascending order of object visibility, effectively reducing hallucinations while retaining visual fidelity. LPOI requires no extra annotations beyond standard pairwise preference data, as it automatically constructs the ranked lists through object masking and interpolation. Comprehensive experiments on MMHalBench, AMBER, and Object HalBench confirm that LPOI outperforms existing preference optimization methods in reducing hallucinations and enhancing VLM performance.
Predicting Through Generation: Why Generation Is Better for Prediction
Authors: Md Kowsher; Nusrat Jahan Prottasha; Prakash Bhat; Chun-Nam Yu; Mojtaba Soltanalian; Ivan Garibay; Ozlem Garibay; Chen Chen; Niloofar Yousefi
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: This paper argues that generating output tokens is more effective than using pooled representations for prediction tasks because token-level generation retains more mutual information. Since LLMs are trained on massive text corpora using next-token prediction, generation aligns naturally with their learned behavior. Using the Data Processing Inequality (DPI), we provide both theoretical and empirical evidence supporting this claim. However, autoregressive models face two key challenges when used for prediction: (1) exposure bias, where the model sees ground-truth tokens during training but relies on its own predictions during inference, leading to errors, and (2) format mismatch, where discrete tokens do not always align with the task’s required output structure. To address these challenges, we introduce PredGen (Predicting Through Generating), an end-to-end framework that (i) uses scheduled sampling to reduce exposure bias, and (ii) introduces a task adapter to convert the generated tokens into structured outputs. Additionally, we introduce Writer-Director Alignment Loss (WDAL), which ensures consistency between token generation and final task predictions, improving both text coherence and numerical accuracy. We evaluate PredGen on multiple classification and regression benchmarks. Our results show that PredGen consistently outperforms standard baselines, demonstrating its effectiveness in structured prediction tasks.
“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization
Authors: Eldar Kurtic; Alexandre Noll Marques; Shubhra Pandit; Mark Kurtz; Dan Alistarh
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Despite the popularity of large language model (LLM) quantization for inference acceleration, significant uncertainty remains regarding the accuracy-performance trade-offs associated with various quantization formats. We present a comprehensive empirical study of quantized accuracy, evaluating popular quantization formats (FP8, INT8, INT4) across academic benchmarks and real-world tasks, on the entire Llama-3.1 model family. Additionally, our study examines the difference in text generated by quantized models versus their uncompressed counterparts. Beyond benchmarks, we also present a couple of quantization improvements which allowed us to obtain state-of-the-art accuracy recovery results. Our investigation, encompassing over 500,000 individual evaluations, yields several key findings: (1) FP8 weight and activation quantization (W8A8-FP) is lossless across all model scales, (2) INT8 weight and activation quantization (W8A8-INT), when properly tuned, incurs surprisingly low 1-3% accuracy degradation, and (3) INT4 weight-only quantization (W4A16-INT) is competitive with 8-bit integer weight and activation quantization. To address the question of the “best” format for a given deployment environment, we conduct inference performance analysis using the popular open-source vLLM framework on various GPU architectures. We find that W4A16 offers the best cost-efficiency for synchronous deployments, and for asynchronous deployment on mid-tier GPUs. At the same time, W8A8 formats excel in asynchronous deployment of mid and large-size models on high-end GPUs. Our results provide a first set of practical guidelines for deploying quantized LLMs across different scales and performance requirements.
StitchLLM: Serving LLMs, One Block at a Time
Authors: Bodun Hu; Shuozhe Li; Saurabh Agarwal; Myungjin Lee; Akshay Jajoo; Jiamin Li; Le Xu; Geon-Woo Kim; Donghyun Kim; Hong Xu; Amy Zhang; Aditya Akella
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The rapid evolution of large language models (LLMs) has revolutionized natural language processing (NLP) tasks such as text generation, translation, and comprehension. However, the increasing computational demands and inference costs of these models present significant challenges. This study investigates the dynamic and efficient utilization of pre-trained weights from open-sourced LLMs of varying parameter sizes to achieve an optimal balance between computational efficiency and task performance. Drawing inspiration from the dual-process theory of human cognition, we introduce StitchLLM: a dynamic model routing framework that employs a powerful bottom model to process all queries, and uses a lightweight routing mechanism to allocate computational resources appropriately. Our novel framework optimizes efficiency and maintains performance, leveraging a trainable stitching layer for seamless integration of decoder layers across different LLMs. Experimental results demonstrate that StitchLLM improves system throughput while minimizing performance degradation, offering a flexible solution for deploying LLMs in resource-constrained settings.
Walk in Others’ Shoes with a Single Glance: Human-Centric Visual Grounding with Top-View Perspective Transformation
Authors: Yuqi Bu; Xin Wu; Zirui Zhao; Yi Cai; David Hsu; Qiong Liu
Venue: ACL 2025 (Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Visual perspective-taking, an ability to envision others’ perspectives from a single self-perspective, is vital in human-robot interactions. Thus, we introduce a human-centric visual grounding task and a dataset to evaluate this ability. Recent advances in vision-language models (VLMs) have shown potential for inferring others’ perspectives, yet are insensitive to information differences induced by slight perspective changes. To address this problem, we propose a top-view enhanced perspective transformation (TEP) method, which decomposes the transition from robot to human perspectives through an abstract top-view representation. It unifies perspectives and facilitates the capture of information differences from diverse perspectives. Experimental results show that TEP improves performance by up to 18%, exhibits perspective-taking abilities across various perspectives, and generalizes effectively to robotic and dynamic scenarios.