GenAI4ELab/papercli-papers huggingface dataset:
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts
Authors: Ming Zhang; Yuhui Wang; Yujiong Shen; Tingyi Yang; Changhao Jiang; Yilong Wu; Shihan Dou; Qinhao Chen; Zhiheng Xi; Zhihao Zhang; Yi Dong; Zhen Wang; Zhihui Fei; Mingyang Wan; Tao Liang; Guojun Ma; Qi Zhang; Tao Gui; Xuanjing Huang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we construct Process Flow Dialogue (PFDial) dataset, which contains 12,705 high-quality Chinese dialogue instructions derived from 440 flowcharts containing 5,055 process nodes. Based on PlantUML specification, each UML flowchart is converted into atomic dialogue units i.e., structured five-tuples. Experimental results demonstrate that a 7B model trained with merely 800 samples, and a 0.5B model trained on total data both can surpass 90% accuracy. Additionally, the 8B model can surpass GPT-4o up to 43.88% with an average of 11.00%. We further evaluate models’ performance on challenging backward transitions in process flows and conduct an in-depth analysis of various dataset formats to reveal their impact on model performance in handling decision and sequential branches. The data is released in https://github.com/KongLongGeFDU/PFDial.
Listening to Patients: Detecting and Mitigating Patient Misreport in Medical Dialogue System
Authors: Lang Qin; Yao Zhang; Hongru Liang; Adam Jatowt; Zhenglu Yang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Medical Dialogue Systems (MDSs) have emerged as promising tools for automated healthcare support through patient-agent interactions. Previous efforts typically relied on an idealized assumption — patients can accurately report symptoms aligned with their actual health conditions. However, in reality, patients often misreport their symptoms, due to cognitive limitations, emotional factors, etc. Overlooking patient misreports can significantly compromise the diagnostic accuracy of MDSs. To address this critical issue, we emphasize the importance of enabling MDSs to “listen to patients” by tackling two key challenges: how to detect misreport and mitigate misreport effectively. In this work, we propose PaMis, a novel framework that can detect patient misreports based on calculating the structural entropy of the dialogue entity graph, and mitigate them through generating controlled clarifying questions. Our experimental results demonstrate that PaMis effectively enhances MDSs reliability by effectively addressing patient misreports during the medical response generation process.
Do Language Models Understand the Cognitive Tasks Given to Them? Investigations with the N-Back Paradigm
Authors: Xiaoyang Hu; Richard Lewis
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Cognitive tasks originally developed for humans are now increasingly used to study language models. While applying these tasks is often straightforward, interpreting their results can be challenging. In particular, when a model underperforms, it is often unclear whether this results from a limitation in the cognitive ability being tested or a failure to understand the task itself. A recent study argues that GPT 3.5’s declining performance on 2-back and 3-back tasks reflects a working memory capacity limit similar to humans (Gong et al., 2024). By analyzing a range of open-source language models of varying performance levels on these tasks, we show that the poor performance is due at least in part to a limitation in task comprehension and task set maintenance. We challenge the best-performing model with progressively harder versions of the task (up to 10-back) and experiment with alternative prompting strategies, before analyzing model attentions. Our larger aim is to contribute to the ongoing conversation around refining methodologies for the cognitive evaluation of language models.
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning
Authors: Yuxia Geng; Runkai Zhu; Jiaoyan Chen; Jintai Chen; Xiang Chen; Zhuo Chen; Shuofei Qiao; Yuxiang Wang; Xiaoliang Xu; Sheng-Jun Huang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the feature divergence of an attribute (resp. object) when combined with different objects (resp. attributes), it is challenging to learn disentangled primitive features that are general across different compositions. To this end, we propose the solution of cross-composition feature disentanglement, which takes multiple primitive-sharing compositions as inputs and constrains the disentangled primitive features to be general across these compositions. More specifically, we leverage a compositional graph to define the overall primitive-sharing relationships between compositions, and build a task-specific architecture upon the recently successful large pre-trained vision-language model (VLM) CLIP, with dual cross-composition disentangling adapters (called L-Adapter and V-Adapter) inserted into CLIP’s frozen text and image encoders, respectively. Evaluation on three popular CZSL benchmarks shows that our proposed solution significantly improves the performance of CZSL, and its components have been verified by solid ablation studies. Our code and data are available at: https://github.com/zhurunkai/DCDA.
Training Long-Context LLMs Efficiently via Chunk-wise Optimization
Authors: Wenhao Li; Yuxin Zhang; Gen Luo; Daohai Yu; Rongrong Ji
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: While long-context large language models (LLMs) exhibit remarkable document processing capabilities, their prohibitively high training costs often hinder customized applications. To mitigate this issue, we propose __Sequential Chunk-wise Optimization (SeCO)__, a memory-efficient training paradigm that partitions lengthy inputs into manageable chunks. Each chunk independently constructs its computational graph and performs localized backpropagation, ensuring that only one chunk’s forward activations are stored in memory. Building on SeCO, we further introduce __Sparse Chunk-wise Optimization (SpaCO)__, which reduces computational overhead by selectively propagating gradients to specific chunks and incorporates a carefully designed compensation factor to ensure unbiased gradient estimation. SpaCO decouples the computational cost of backpropagation from the context length, enabling training time to gradually converge to inference time as sequences become longer. Implemented as lightweight training wrappers, both SeCO and SpaCO offer substantial practical benefits. For example, when fine-tuning an 8B model with LoRA on a single RTX 3090 GPU, SeCO expands maximum sequence length from 1K to 16K tokens, while SpaCO demonstrates accelerated training speed—achieving up to 3× faster than SeCO under the same experimental setup. These innovations provide new insights into optimizing long-context models, making them more accessible for practical applications. We have open-sourced the code at https://anonymous.4open.science/r/seco-CCBD.
Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps
Authors: Jiashun Cheng; Aochuan Chen; Nuo Chen; Ziqi Gao; Yuhan Li; Jia Li; Fugee Tsung
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Low-Rank Adaptation (LoRA) has emerged as a prominent technique for fine-tuning large foundation models. Despite its successes, the substantial parameter redundancy, which limits the capacity and efficiency of LoRA, has been recognized as a bottleneck. In this work, we systematically investigate the impact of redundancy in fine-tuning LoRA and reveal that reducing density redundancy does not degrade expressiveness. Based on this insight, we introduce Spectral-encoding Low-Rank Adaptation (SeLoRA), which harnesses the robust expressiveness of spectral bases to re-parameterize LoRA from a sparse spectral subspace. Designed with simplicity, SeLoRA enables seamless integration with various LoRA variants for performance boosting, serving as a scalable plug-and-play framework. Extensive experiments substantiate that SeLoRA achieves greater efficiency with fewer parameters, delivering superior performance enhancements over strong baselines on various downstream tasks, including commonsense reasoning, math reasoning, and code generation.
CODEMENV: Benchmarking Large Language Models on Code Migration
Authors: Keyuan Cheng; Xudong Shen; Yihao Yang; Tengyue Wang; Yang Cao; Muhammad Asif Ali; Hanbin Wang; Lijie Hu; Di Wang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large language models (LLMs) have demonstrated remarkable proficiency in handling a wide range of tasks within the software engineering domain, but their ability to perform code migration—adapting code to different environments—remains underexplored. In this work, we propose a novel benchmark, : Code Migration Across Environment, designed to evaluate LLMs’ performance in handling code migration tasks. The benchmark comprises 922 data points across 19 Python and Java packages, offering three tasks to systematically evaluate code migration: identifying version-incompatible functions, determining function changes, and adapting code to target environments. Experimental evaluation of across seven LLMs revealed an average pass@1 rate of 26.50%, with GPT-4o performing best at 43.84%. We highlight our key findings as follows: (i) LLMs are more familiar with newer function versions, making them better at migrating legacy code, and (ii) a logical inconsistency where LLMs sometimes identify irrelevant function changes for the target migration environment.
A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs
Authors: V.S.D.S.Mahesh Akavarapu; Hrishikesh Terdalkar; Pramit Bhattacharyya; Shubhangi Agarwal; Dr. Vishakha Deulgaonkar; Chaitali Dangarikar; Pralay Manna; Arnab Bhattacharya
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across diverse tasks and languages. In this study, we focus on natural language understanding in three classical languages—Sanskrit, Ancient Greek and Latin—to investigate the factors affecting cross-lingual zero-shot generalization. First, we explore named entity recognition and machine translation into English. While LLMs perform equal to or better than fine-tuned baselines on out-of-domain data, smaller models often struggle, especially with niche or abstract entity types. In addition, we concentrate on Sanskrit by presenting a factoid question–answering (QA) dataset and show that incorporating context via retrieval-augmented generation approach significantly boosts performance. In contrast, we observe pronounced performance drops for smaller LLMs across these QA tasks. These results suggest model scale as an important factor influencing cross-lingual generalization. Assuming that models used such as GPT-4o and Llama-3.1 are not instruction fine-tuned on classical languages, our findings provide insights into how LLMs may generalize on these languages and their consequent utility in classical studies.
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation
Authors: Jilong Li; Zhenxi Song; Jiaqi Wang; Meishan Zhang; Honghai Liu; Min Zhang; Zhiguo Zhang
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Current EEG/MEG-to-text decoding systems suffer from three key limitations: (1) reliance on teacher-forcing methods, which compromises robustness during inference, (2) sensitivity to session-specific noise, hindering generalization across subjects, and (3) misalignment between brain signals and linguistic representations due to pre-trained language model over-dominance. To overcome these challenges, we propose BrainECHO (Brain signal decoding via vEctor-quantized speCtrogram reconstruction for WHisper-enhanced text generatiOn), a multi-stage framework that employs decoupled representation learning to achieve state-of-the-art performance on both EEG and MEG datasets. Specifically, BrainECHO consists of three stages: (1) Discrete autoencoding, which transforms continuous Mel spectrograms into a finite set of high-quality discrete representations for subsequent stages. (2) Frozen alignment, where brain signal embeddings are mapped to corresponding Mel spectrogram embeddings in a frozen latent space, effectively filtering session-specific noise through vector-quantized reconstruction, yielding a 3.65% improvement in BLEU-4 score. (3) Constrained decoding fine-tuning, which leverages the pre-trained Whisper model for audio-to-text translation, balancing signal adaptation with knowledge preservation, and achieving 74%-89% decoding BLEU scores without excessive reliance on teacher forcing. BrainECHO demonstrates robustness across sentence, session, and subject-independent conditions, passing Gaussian noise tests and showcasing its potential for enhancing language-based brain-computer interfaces.
Progressive LoRA for Multimodal Continual Instruction Tuning
Authors: Yahan Yu; Duzhen Zhang; Yong Ren; Xuanle Zhao; Xiuyi Chen; Chenhui Chu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Multimodal Continual Instruction Tuning (MCIT) empowers Multimodal Large Language Models (MLLMs) to adapt to ever-evolving requirements without continuous costly retraining. However, MCIT faces challenges in mitigating Catastrophic Forgetting (CF) and enhancing Knowledge Transfer (KT). Existing works combine Mixture-of-Expert (MoE) and LoRA to address these. However, using a fixed number of shared LoRA blocks across tasks can lead to the overwriting of acquired knowledge, making MLLMs harder to handle CF and KT. Therefore, we propose the **Prog**ressive **LoRA** framework (ProgLoRA), which contains a progressive LoRA pool and trains a new LoRA block for each incremental task to reduce knowledge interference. Specifically, ProgLoRA has two key mechanisms: task-aware allocation for effectively leveraging acquired knowledge at current task and task recall for realigning the model with learned tasks. Additionally, considering different application scenarios, we design a static ProgLoRA for the more idealized basic setting and a dynamic ProgLoRA for the more realistic challenging setting. Experiments on the latest MCIT benchmark demonstrate that ProgLoRA outperforms existing approaches.
ARC ‘Challenge’ Is Not That Challenging
Authors: Łukasz Borchmann
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: ARC Challenge appears more difficult than ARC Easy for modern LLMs primarily due to an evaluation setup that prevents direct comparison of answer choices rather than inherent complexity. Although some researchers have quietly shifted to a more appropriate scheme over the last year, the implications of this change have yet to be widely acknowledged. We highlight this overlooked shift, show how similar evaluation practices falsely imply reasoning deficits in other benchmarks, and demonstrate that fairer methods dramatically reduce performance gaps (e.g. on SIQA) and even yield superhuman results (OpenBookQA). In doing so, we reveal how evaluation shapes perceived difficulty and offer guidelines to ensure that multiple-choice evaluations accurately reflect actual model capabilities.
Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation
Authors: Vera Neplenbroek; Arianna Bisazza; Raquel Fernández
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Recent generative large language models (LLMs) show remarkable performance in non-English languages, but when prompted in those languages they tend to express higher harmful social biases and toxicity levels. Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in English can transfer to other languages. In this work, we investigate the impact of different finetuning methods on the model’s bias and toxicity, but also on its ability to produce fluent and diverse text. We reduce biases by finetuning on curated non-harmful text, but find only direct preference optimization to be effective for mitigating toxicity. The mitigation caused by applying these methods in English also transfers to non-English languages. We find evidence that the extent to which transfer takes place can be predicted by the amount of data in a given language present in the model’s pretraining data. However, this transfer of bias and toxicity mitigation often comes at the expense of decreased language generation ability in non-English languages, highlighting the importance of developing language-specific bias and toxicity mitigation methods.
Tracr-Injection: Distilling Algorithms into Pre-trained Language Models
Authors: Tomás Vergara Browne; Alvaro Soto
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Motivated by the surge of large language models, there has been a push to formally characterize the symbolic abilities intrinsic to the transformer architecture. A programming language, called RASP, has been proposed, which can be directly compiled into transformer weights to implement these algorithms. However, the tasks that can be implemented in RASP are often uncommon to learn from natural unsupervised data, showing a mismatch between theoretical capabilities of the transformer architecture, and the practical learnability of these capabilities from unsupervised data. We propose tracr-injection, a method that allows us to distill algorithms written in RASP directly into a pre-trained language model. We showcase our method by injecting 3 different algorithms into a language model. We show how our method creates an interpretable subspace within the model’s residual stream, which can be decoded into the variables present in the code of the RASP algorithm. Additionally, we found that the proposed method can improve out-of-distribution performance compared to our baseline, indicating that indeed a more symbolic mechanism is taking place in the inner workings of the model. We release the code used to run our experiments.
Model Performance-Guided Evaluation Data Selection for Effective Prompt Optimization
Authors: Ximing Dong; Shaowei Wang; Dayi Lin; Ahmed Hassan
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Optimizing Large Language Model (LLM) performance requires well-crafted prompts, but manual prompt engineering is labor-intensive and often ineffective. Automated prompt optimization techniques address this challenge but the major of them rely on randomly selected evaluation subsets, which fail to represent the full dataset, leading to unreliable evaluations and suboptimal prompts. Existing coreset selection methods, designed for LLM benchmarking, are unsuitable for prompt optimization due to challenges in clustering similar samples, high data collection costs, and the unavailability of performance data for new or private datasets. To overcome these issues, we propose IPOMP, an Iterative evaluation data selection approach for effective Prompt Optimization using real time Model Performance. IPOMP is a two-stage approach that selects representative and diverse samples using semantic clustering and boundary analysis, followed by iterative refinement with real-time model performance data to replace redundant samples. Evaluations on two datasets BIG-bench and LIAR, and two models GPT-3.5 and GPT-4o-mini, show that IPOMP improves effectiveness by at least 1.6% to 3.1%, and stability by at least 50% to 55.5% compared with the best baseline across the studied datasets and models, with minimal computational overhead below 1%. Furthermore, the results demonstrate that our real-time performance-guided refinement approach can be universally applied to enhance existing coreset selection methods.
Revisiting Weak-to-Strong Generalization in Theory and Practice: Reverse KL vs. Forward KL
Authors: Wei Yao; Wenkai Yang; Ziqiao Wang; Yankai Lin; Yong Liu
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: As large language models advance toward superhuman performance, ensuring their alignment with human values and abilities grows increasingly complex. Weak-to-strong generalization offers a promising approach by leveraging predictions from weaker models to guide stronger systems, but its effectiveness could be constrained by the inherent noise and inaccuracies in these weak predictions. To address this, we propose a theoretically grounded approach that replaces forward KL divergence—whose mass-covering behavior risks overfitting to imperfect weak signals—with reverse KL divergence. Reverse KL divergence’s zero-forcing effect prioritizes high-confidence predictions, effectively mitigating the influence of unreliable weak supervision. Theoretically, we extend existing bounds and derive tighter lower bounds for both forward and reverse KL divergence. Notably, when a sufficiently pre-trained strong model is fine-tuned on the last linear layer, reverse KL guarantees that it outperforms its weak supervisor by the magnitude of their disagreement. Empirically, we demonstrate that reverse KL and reverse cross-entropy not only enable strong models to outperform those trained with forward KL and standard cross-entropy across most settings, but also exhibit greater robustness to noisy labels.
Stories that (are) Move(d by) Markets: A Causal Exploration of Market Shocks and Semantic Shifts across Different Partisan Groups
Authors: Felix Drinkall; Stefan Zohren; Michael McMahon; Janet B. Pierrehumbert
Venue: ACL 2025 (Findings of the Association for Computational Linguistics: ACL 2025)

Abstract: Macroeconomic fluctuations and the narratives that shape them form a mutually reinforcing cycle: public discourse can spur behavioural changes leading to economic shifts, which then result in changes in the stories that propagate. We show that shifts in semantic embedding space can be causally linked to real-world market shocks or deviations from the expected market behaviour (sec:market_shocks). Furthermore, we show how partisanship can influence the predictive power of text for market fluctuations and shape reactions to those same shocks. We also provide some evidence that text-based signals are particularly salient during rare events such as COVID-19, highlighting the value of language data as an exogenous variable in economic forecasting. Our findings underscore the bidirectional relationship between news outlets and market shocks, offering a novel empirical approach to studying their effect on each other.