ACL 2025 论文列表

This paper introduces the Balanced Arabic Readability Evaluation Corpus (BAREC), a large-scale, fine-grained dataset for Arabic readability assessment. BAREC consists of 69,441 sentences spanning 1+ million words, carefully curated to cover 19 readability levels, from kindergarten to postgraduate comprehension. The corpus balances genre diversity, topical coverage, and target audiences, offering a comprehensive resource for evaluating Arabic text complexity. The corpus was fully manually annotated by a large team of annotators. The average pairwise inter-annotator agreement, measured by Quadratic Weighted Kappa, is 81.8%, reflecting a high level of substantial agreement.Beyond presenting the corpus, we benchmark automatic readability assessment across different granularity levels, comparing a range of techniques. Our results highlight the challenges and opportunities in Arabic readability modeling, demonstrating competitive performance across various methods.To support research and education, we make BAREC openly available, along with detailed annotation guidelines and benchmark results: http://barec.camel-lab.com.

Large Language Models (LLMs) augmented with external tools have demonstrated remarkable capabilities in complex problem solving. Existing efforts for tool utilization typically involve an LLM agent that contains instructions on using the description of the available tools to determine and call the tools required to solve the problem. Inference Scaling techniques, such as chain-of-thought and tree-of-thought reasoning, are commonly used but require significant computational overhead and rendering such methods impractical in real-world applications. In this work, we recognize and formalize the critical role of instructions provided in agent prompts and tool descriptions—collectively referred to as *context*—and show that incomplete *context* is one of the reasons for this computational overhead.To fill this efficiency gap, we propose an optimization framework that jointly refines both the instructions provided in the agent prompt and tool description, enhancing their interaction. Experiments on StableToolBench and RestBench demonstrate that our optimized agents achieve superior efficiency while maintaining effectiveness. Our findings underscore the critical role of context optimization in improving LLM agents for tool utilization, paving the way for more responsive and cost-effective LLM agents. Our code is available at [https://github.com/Bingo-W/ToolOptimization](https://github.com/Bingo-W/ToolOptimization).

Recent Continual Learning (CL)-based Temporal Knowledge Graph Reasoning (TKGR) methods focus on significantly reducing computational cost and mitigating catastrophic forgetting caused by fine-tuning models with new data. However, existing CL-based TKGR methods still face two key limitations: (1) They usually one-sidedly reorganize individual historical facts, while overlooking the historical context essential for accurately understanding the historical semantics of these facts; (2) They preserve historical knowledge by simply replaying historical facts, while ignoring the potential conflicts between historical and emerging facts. In this paper, we propose a Deep Generative Adaptive Replay (DGAR) method, which can generate and adaptively replay historical entity distribution representations from the whole historical context. To address the first challenge, historical context prompts as sampling units are built to preserve the whole historical context information. To overcome the second challenge, a pre-trained diffusion model is adopted to generate the historical distribution. During the generation process, the common features between the historical and current distributions are enhanced under the guidance of the TKGR model. In addition, a layer-by-layer adaptive replay mechanism is designed to effectively integrate historical and current distributions. Experimental results demonstrate that DGAR significantly outperforms baselines in reasoning and mitigating forgetting.

ICD Coding aims to assign a wide range of medical codes to a medical text document, which is a popular and challenging task in the healthcare domain. To alleviate the problems of long-tail distribution and the lack of annotations of code-specific evidence, many previous works have proposed incorporating code knowledge to improve coding performance. However, existing methods often focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other, thereby limiting their scalability and effectiveness. To address this issue, we propose GKI-ICD, a novel, general knowledge injection framework that integrates three key types of knowledge, namely ICD Description, ICD Synonym, and ICD Hierarchy, without specialized design of additional modules. The comprehensive utilization of the above knowledge, which exhibits both differences and complementarity, can effectively enhance the ICD coding performance. Extensive experiments on existing popular ICD coding benchmarks demonstrate the effectiveness of GKI-ICD, which achieves the state-of-the-art performance on most evaluation metrics. Code is available at https://github.com/xuzhang0112/GKI-ICD.

Unlearning has been proposed to remove copyrighted and privacy-sensitive data from Large Language Models (LLMs). Existing approaches primarily rely on fine-tuning-based methods, which can be categorized into gradient ascent-based (GA-based) and suppression-based methods. However, they often degrade model utility (the ability to respond to normal prompts). In this work, we aim to develop a general framework that enhances the utility of fine-tuning-based unlearning methods. To achieve this goal, we first investigate the common property between GA-based and suppression-based methods. We unveil that GA-based methods unlearn by distinguishing the target data (i.e., the data to be removed) and suppressing related generations—essentially the same strategy employed by suppression-based methods. Inspired by this finding, we introduce Gated Representation UNlearning (GRUN) which has two components: a soft gate function for distinguishing target data and a suppression module using Representation Fine-tuning (ReFT) to adjust representations rather than model parameters. Experiments show that GRUN significantly improves the unlearning and utility. Meanwhile, it is general for fine-tuning-based methods, efficient and promising for sequential unlearning.

The conversational capabilities of Large Language Models (LLMs) suggest that they may be able to perform as automated talk therapists. It is crucial to know if these systems would be effective and adhere to known standards. We present a counsellor chatbot that focuses on motivating tobacco smokers to quit smoking. It uses a state-of-the-art LLM and a widely applied therapeutic approach called Motivational Interviewing (MI), and was evolved in collaboration with clinician-scientists with expertise in MI. We also describe and validate an automated assessment of both the chatbot’s adherence to MI and client responses. The chatbot was tested on 106 participants, and their confidence that they could succeed in quitting smoking was measured before the conversation and one week later. Participants’ confidence increased by an average of 1.7 on a 0-10 scale. The automated assessment of the chatbot showed adherence to MI standards in 98% of utterances, higher than human counsellors. The chatbot scored well on a participant-reported metric of perceived empathy but lower than typical human counsellors. Furthermore, participants’ language indicated a good level of motivation to change, a key goal in MI. These results suggest that the automation of talk therapy with a modern LLM has promise.

Recent advances in Large Language Models (LLMs) have shown promise in automating discourse annotation for conversations. While manually designing tree annotation schemes significantly improves annotation quality for humans and models, their creation remains time-consuming and requires expert knowledge. We propose a fully automated pipeline that uses LLMs to construct such schemes and perform annotation. We evaluate our approach on speech functions (SFs) and the Switchboard-DAMSL (SWBD-DAMSL) taxonomies. Our experiments compare various design choices, and we show that frequency-guided decision trees, paired with an advanced LLM for annotation, can outperform previously manually designed trees and even match or surpass human annotators while significantly reducing the time required for annotation. We release all code and resultant schemes and annotations to facilitate future research on discourse annotation.

In NLG meta-evaluation, evaluation metrics are typically assessed based on their consistency with humans. However, we identify some limitations in traditional NLG meta-evaluation approaches, such as issues in handling human ratings and ambiguous selections of correlation measures, which undermine the effectiveness of meta-evaluation. In this work, we propose a dual-perspective NLG meta-evaluation framework that focuses on different evaluation capabilities, thereby providing better interpretability. In addition, we introduce a method of automatically constructing the corresponding benchmarks without requiring new human annotations. Furthermore, we conduct experiments with 16 representative LLMs as the evaluators based on our proposed framework, comprehensively analyzing their evaluation performance from different perspectives.

Negotiation agents need to influence the attitudes or intentions of users to reach a consensus. Strategy planning and expressive optimization are crucial aspects of effective negotiations. However, previous studies have typically focused on only one of these aspects, neglecting the fact that their combined synergistic effect can lead to better performance. Inspired by the dual-process theory in human cognition, we propose a Dual-Mind Negotiation Agent (DMNA) framework. This framework integrates an intuitive module for rapid, experience-based response and a deliberative module for slow, expression optimization. The intuitive module is trained using Monte Carlo Tree Search (MCTS) and Direct Preference Optimization (DPO), enabling it to make suitable strategic planning and expression. The deliberative module employs a multifaceted reflexion mechanism to enhance the quality of expression. Experiments conducted on negotiation datasets confirm that DMNA achieves state-of-the-art results, demonstrating an enhancement in the negotiation ability of agents.

Large Language Models (LLMs) are cutting-edge generative AI models built on transformer architecture, which tend to be highly memory-intensive when performing real-time inference. Various strategies have been developed to enhance the end-to-end inference speed for LLMs, one of which is speculative decoding. This technique involves running a smaller LLM (draft model) for inference over a defined window size, denoted as 𝛾, while simultaneously being validated by the larger LLM (target model). Choosing the optimal 𝛾 value and the draft model is essential for unlocking the potential of speculative decoding. But it is difficult to do due to the complicated influence from various factors, including the nature of the task, the hardware in use, and the combination of the large and small models. This paper introduces *on-the-fly adaption of speculative decoding*, a solution that dynamically adapts the choices to maximize the efficiency of speculative decoding for LLM inferences. As a drop-in solution, it needs no offline benchmarking or training. Experiments show that the solution can lead to 3.55-16.48% speed improvement over the standard speculative decoding, and 1.2-3.4× over the default LLMs.

We explore the role of the visual modality and of vision transformers in predicting the compositionality of English noun compounds. Crucially, we contribute a framework to address the challenge of obtaining adequate images that represent non-compositional compounds (such as “couch potato”), making it relevant for any image-based approach targeting figurative language. Our method uses prompting strategies and diffusion models to generate images. Comparing and combining our approach with a state-of-the-art text-based approach reveals complementary contributions regarding features as well as degrees of abstractness in compounds.

Existing text revision systems are capable of generating fluent and coherent text, but struggle with constrained text revision (CTR), which requires adherence to specific constraints. Furthermore, adapting these systems to diverse constraints is challenging. To bridge this gap, we introduce TRIPS, a Text Revision agent via Iterative Planning and Searching, focusing on CTR. TRIPS utilizes a planner, a reviser (i.e., a large language model), and adaptable tools to generate revisions tailored to different scenarios. Specifically, we propose an iterative self-training alignment method to construct the planner, which generates tool usage and text revision plans. Furthermore, we propose Tool-Guided Monte Carlo Tree Search (TG-MCTS), a novel CTR algorithm that extends MCTS with tool-guided expansion and evaluation, enabling the search for optimal revision strategies across various scenarios. To evaluate TRIPS, we introduce ConsTRev, a dataset with multi-level constrained instructions for paragraph-level revision. Experimental results show that TRIPS outperforms baselines in both constraint adherence and revision quality. Furthermore, TRIPS exhibits robust performance across diverse use cases, including plain text and LaTeX revision.

This study explores current limitations of learned image captioning evaluation metrics, specifically the lack of granular assessments for errors within captions, and the reliance on single-point quality estimates without considering uncertainty. To address the limitations, we propose a simple yet effective strategy for generating and calibrating distributions of CLIPScore values. Leveraging a model-agnostic conformal risk control framework, we calibrate CLIPScore values for task-specific control variables, tackling the aforementioned limitations. Experimental results demonstrate that using conformal risk control, over score distributions produced with simple methods such as input masking, can achieve competitive performance compared to more complex approaches. Our method effectively detects erroneous words, while providing formal guarantees aligned with desired risk levels. It also improves the correlation between uncertainty estimations and prediction errors, thus enhancing the overall reliability of caption evaluation metrics.

Recent advancements in retrieval-augmented generation (RAG) have enhanced large language models in question answering by integrating external knowledge. However, challenges persist in achieving global understanding and aligning responses with human ethical and quality preferences. To address these issues, we propose GraphMPA, a comprehensive graph-based framework with mode-seeking preference alignment. Our approach constructs a hierarchical document graph using a general similarity measurement, mimicking human cognitive processes for information understanding and synthesis. Additionally, we introduce mode-seeking preference optimization to better align model outputs with human preferences through probability-matching constraints. Extensive experiments on six datasets demonstrate the effectiveness of our GraphMPA.

Like humans, Large Language Models (LLMs) struggle to generate high-quality long-form text that adheres to strict requirements in a single pass. This challenge is unsurprising, as successful human writing, according to the Cognitive Writing Theory, is a complex cognitive process involving iterative planning, translating, reviewing, and monitoring. Motivated by these cognitive principles, we aim to equip LLMs with human-like cognitive writing capabilities through CogWriter, a novel training-free framework that transforms LLM constrained long-form text generation into a systematic cognitive writing paradigm. Our framework consists of two key modules: (1) a Planning Agent that performs hierarchical planning to decompose the task, and (2) multiple Generation Agents that execute these plans in parallel. The system maintains quality via continuous monitoring and reviewing mechanisms, which evaluate outputs against specified requirements and trigger necessary revisions. CogWriter demonstrates exceptional performance on LongGenBench, a benchmark for complex constrained long-form text generation. Even when using Qwen-2.5-14B as its backbone, CogWriter surpasses GPT-4o by 22% in complex instruction completion accuracy while reliably generating texts exceeding 10,000 words. We hope this cognitive science-inspired approach provides a paradigm for LLM writing advancements: https://anonymous.4open.science/r/CogWriter-8DFE.

The current project is inscribed within the field of stemmatology or the study and/or reconstruction of textual transmission based on the relationship between the available witnesses of given texts. In particular, the variants (differences) at the word-level in manuscripts written in Biblical Hebrew are considered. A strong classifier (F1 value of 0.80) is trained to predict the category of difference between word pairs (‘plus/minus’, ‘inversion’, ‘morphological’, ‘lexical’ or ‘unclassifiable’) as present in collated (aligned) pairs of witnesses. The classifier is non-neural and makes use of the two words themselves as well as part-of-speech (POS) tags, hand-crafted rules per category and synthetically derived data. Other models experimented with include neural ones based on the state-of-the-art model for Modern Hebrew, DictaBERT. Other features whose relevance is tested are different types of morphological information pertaining to the word pairs and the Levenshtein distance between words. A selection of the strongest classifiers as well as the used synthetic data and the steps taken at its derivation are made available. Coincidentally, the corelation between two sets of morphological labels is investigated: professionally established as per the Qumran-Digital online library and automatically derived with the sub-model DictaBERT-morph.

Creative story generation has long been a goal of NLP research. While existing methodologies have aimed to generate long and coherent stories, they fall significantly short of human capabilities in terms of diversity and character depth. To address this, we introduce a novel story generation framework called CCI (Character-centric Creative story generation via Imagination). CCI features two modules for creative story generation: IG (Image-Guided Imagination) and MW (Multi-Writer model). In the IG module, we utilize a text-to-image model to create visual representations of key story elements, such as characters, backgrounds, and main plots, in a more novel and concrete manner than text-only approaches. The MW module uses these story elements to generate multiple persona-description candidates and selects the best one to insert into the story, thereby enhancing the richness and depth of the narrative. We compared the stories generated by CCI and baseline models through statistical analysis, as well as human and LLM evaluations. The results showed that the IG and MW modules significantly improve various aspects of the stories’ creativity. Furthermore, our framework enables interactive multi-modal story generation with users, opening up new possibilities for human-LLM integration in cultural development. Project page : https://www.2024cci.p-e.kr/

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.

Recently, many studies have demonstrated that exclusively incorporating OCR-derived text and spatial layouts with large language models (LLMs) can be highly effective for document understanding tasks. However, existing methods that integrate spatial layouts with text have limitations, such as producing overly long text sequences or failing to fully leverage the autoregressive traits of LLMs. In this work, we introduce Interleaving Layout andText in a Large Language Model (LayTextLLM) for document understanding. LayTextLLM projects each bounding box to a single embedding and interleaves it with text, efficiently avoiding long sequence issues while leveraging autoregressive traits of LLMs. LayTextLLM not only streamlines the interaction of layout and textual data but also shows enhanced performance in KIE and VQA. Comprehensive benchmark evaluations reveal significant improvements of LayTextLLM, with a 15.2% increase on KIE tasks and 10.7% on VQA tasks compared to previous SOTA OCR-based LLMs. All resources are available at URL masked for anonymous review.