Austria

Large Language Models (LLMs) recently achieved great success in medical text summarization by simply using in-context learning. However, these recent efforts do not perform fine-grained evaluations under difficult settings where LLMs might fail. They typically report performance scores over the entire dataset. Through our benchmarking study, we show that LLMs show a significant performance drop for data points with high concentration of out-of-vocabulary (OOV) words or with high novelty. Vocabulary adaptation is an intuitive solution to this vocabulary mismatch issue where the LLM vocabulary gets updated with certain expert domain (here, medical) words or subwords. An interesting finding from our study is that Llama-3.1, even with a vocabulary size of around 128K tokens, still faces _over-fragmentation_ issue with medical words. To that end, we show vocabulary adaptation helps improve the LLM summarization performance even in difficult settings. Through extensive experimentation of multiple vocabulary adaptation strategies, two continual pretraining strategies, and three benchmark medical summarization datasets, we gain valuable insights into the role of vocabulary adaptation strategies for customizing LLMs to the medical domain. We also performed a human evaluation study with medical experts where they found that vocabulary adaptation results in more relevant and faithful summaries. Our codebase is made publicly available at https://github.com/gb-kgp/LLM-MedicalSummarization-Benchmark.

ACL 2025

Evaluation of LLMs in Medical Text Summarization: The Role of Vocabulary Adaptation in High OOV Settings

medical query-focused summarization

benchmarking llms

vocabulary adaptation

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

poster

Writing is inherently iterative, each revision enhancing information representation. One revision may contain many edits. Examination of the intentions behind edits provides valuable insights into an editor’s expertise, the dynamics of collaborative writing, and the evolution of a document. Current research on edit intentions lacks a comprehensive edit intention taxonomy (EIT) that spans multiple application domains. As a result, researchers often create new EITs tailored to specific needs, a process that is both time-consuming and costly. To address this gap, we propose UniT, a Unified edit intention Taxonomy that integrates existing EITs encompassing a wide range of edit intentions. We examine the lineage relationship and the construction of 24 EITs. They together have 232 categories across various domains. During the literature survey and integration process, we identify challenges such as one-to-many category matches, incomplete definitions, and varying hierarchical structures. We propose solutions for resolving these issues. Finally, our evaluation shows that our UniT achieves higher inter-annotator agreement scores compared to existing EITs and is applicable to a large set of application domains.

UniT: One Document, Many Revisions, Too Many Edit Intention Taxonomies

Detecting toxicity in online multimodal environments such as memes remains a challenging task due to the complex contextual connections across modalities (e.g., text and visual), demanding both common sense reasoning and contextual awareness. To bridge this gap, we propose a hybrid neurosymbolic framework that unifies (1) distillation of implicit contextual knowledge (e.g., sarcasm, cultural references) from Large Vision-Language Models (LVLMs) and (2) infusion of explicit relational semantics through sub-graphs from Knowledge Graphs (KGs). Experimental results on two benchmark datasets show the superior performance of our approach, Knowledge-Infused Distilled Vision-Language Model (KID-VLM), over the state-of-the-art baselines across AUC, and F1, with improvements of 0.5%, and 10.6%, respectively in HatefulMemes Benchmark across variants. Further, KID-VLM demonstrates better generalizability and achieves best performance across all baselines in HarMeme Dataset with an 6.3% and 3.2% in F1 and AUC. Given the contextual complexity of the toxicity detection task, KID-VLM showcases the significance of learning compact models (~500M parameters) from both explicit (i.e., KG) and implicit (i.e., LVLMs) contextual cues incorporated through a hybrid neurosymbolic approach. KID-VLM provides an accurate and scalable approach to recognize toxic content for safer online environments in real-world applications. Our code is available at Anonymous Github at: https://anonymous.4open.science/r/MultimodalToxicity-124F/README.md.

Just KIDDIN’ : Knowledge Infusion and Distillation for Detection of INdecent Memes

Open-world planning poses a significant challenge for general artificial intelligence due to environmental complexity and task diversity, especially in long-term tasks and lifelong learning. Inspired by cognitive theories, we propose M2PA, an open-world multi-memory planning agent. M2PA innovates by combining Large Language Models (LLMs) with human-like multi-memory systems, aiming to fully leverage the strengths of both while mitigating their respective limitations. By integrating the expansive world knowledge and language processing capabilities of LLMs with the perception and experience accumulation abilities of the human memory system, M2PA exhibits situation awareness, and experience generalization capabilities, as well as the potential for lifelong learning. In experiments, M2PA significantly outperforms current state-of-the-art agents across 50 Minecraft tasks in zero-shot learning. In exploratory lifelong learning experiments, M2PA demonstrates its continuous learning ability, achieving a \textbf{38.33%} success rate in the “ObtainDiamond” task. Our findings provide a novel paradigm for constructing more effective agents in open-world environments.

M2PA: A Multi-Memory Planning Agent for Open Worlds Inspired by Cognitive Theory

Decompilers are fundamental tools for critical security tasks, from vulnerability discovery to malware analysis, yet their evaluation remains fragmented. Existing approaches primarily focus on syntactic correctness through synthetic micro-benchmarks or subjective human ratings, failing to address real-world requirements for semantic fidelity and analyst usability. We present **DecompileBench**, the first comprehensive framework that enables effective evaluation of decompilers in reverse engineering workflows through three key components: real-world function extraction (comprising 23,400 functions from 130 real-world programs), runtime-aware validation, and automated human-centric assessment using LLM-as-Judge to quantify the effectiveness of decompilers in reverse engineering workflows. Through a systematic comparison between six industrial-strength decompilers and six recent LLM-powered approaches, we demonstrate that LLM-based methods surpass commercial tools in code understandability despite 52.2% lower functionality correctness. These findings highlight the potential of LLM-based approaches to transform human-centric reverse engineering. We open source **DecompileBench** to provide a framework to advance research on decompilers and assist security experts in making informed tool selections based on their specific requirements.

DecompileBench: A Comprehensive Benchmark for Evaluating Decompilers in Real-World Scenarios

Code generation is crucial in software engineering for automating the coding process efficiently. 
While test-time computation methods show promise, they suffer from high latency due to multiple computation rounds.
To overcome this, we introduce \textbf{ThinkCoder}, a framework that combines thorough exploration with optimal refinement.
The exploration phase diversifies the solution space by searching for potential solutions, followed by a refinement phase that enhances precision.
This approach allows us to select the best solution through careful consideration before taking action, avoiding excessive trial and error.
To further minimize test-time computation overhead, we introduce preference-driven optimization with Reinforced Self-Training (ReST), which uses exploration trajectories from ThinkCoder to guide LLM's evolution.
This approach enhances LLM's exploration efficiency via preference learning, cutting costs while maintaining accuracy.
ThinkCoder boosts the performance with a single LLM, excelling on benchmarks like HumanEval and MBPP. 
Compared to SOTA models, it improves Pass@1 by 3.0\% over MapCoder with just 6.4\% of the computation cost.
Against AgentCoder, ThinkCoder achieves a 0.5\% higher Pass@1 after 2 rounds, outperforming AgentCoder's 5 rounds.
Additionally, ReST with success trajectories enhances efficiency, allowing models like LLaMA2-7B to achieve competitive results using only 20\% of the computational resources. 
These results highlight the framework's effectiveness and scalability. 
\footnote{The code is available at \url{https://github.com/xiaoqzhwhu/ThinkCoder}}

Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement

Hallucination is a persistent challenge in large language models (LLMs), where even with rigorous quality control, models often generate distorted facts. This paradox, in which error generation continues despite high-quality training data, calls for a deeper understanding of the underlying LLM mechanisms. To address it, we propose a novel concept: knowledge overshadowing, where model’s dominant knowledge can obscure less prominent knowledge during text generation, causing the model to fabricate inaccurate details. Building on this idea, we introduce a novel framework to quantify factual hallucinations by modeling knowledge overshadowing. Central to our approach is the log-linear law, which predicts that the rate of factual hallucination increases linearly with the logarithmic scale of (1) Knowledge Popularity, (2) Knowledge Length, and (3) Model Size. The law provides a means to preemptively quantify hallucinations, offering foresight into their occurrence even before model training or inference. Built on overshadowing effect, we propose a new decoding strategy CoDa, to mitigate hallucinations, which notably enhance model factuality on Overshadow (27.9%), MemoTrap (13.1%) and NQ-Swap (18.3%). Our findings not only deepen understandings of the underlying mechanisms behind hallucinations but also provide actionable insights for developing more predictable and controllable language models.

The Law of Knowledge Overshadowing: Towards Understanding, Predicting and Preventing LLM Hallucination

Large Language Models (LLMs) have demonstrated impressive performances in tasks related to coreference resolution. However, previous studies mostly assessed LLM performance on coreference resolution with nouns and third person pronouns. This study evaluates LLM performance on coreference resolution with indexical like I, you, here and tomorrow} which come with unique challenges due to their linguistic properties. We present the first study examining how LLMs interpret indexicals in English, releasing the English Indexical Dataset with 1600 multiple-choice questions. We evaluate pioneering LLMs, including GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, and DeepSeek V3. Our results reveal that LLMs exhibit an impressive performance with some indexicals (I), while struggling with others (you, here, tomorrow), and that syntactic cues (e.g. quotation) contribute to LLM performance with some indexicals, while they reduce performance with others.

Un-considering Contextual Information: Assessing LLMs’ Understanding of Indexical Elements

Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot scenarios. While prior work has noted this performance gap, the behavioral factors driving the performance gap remain under-explored. This study proposes a comprehensive evaluation framework to quantify the behavior gap between AI agents and human experts, focusing on discrepancies in dialog acts, tool usage, and knowledge utilization. Our findings reveal that this behavior gap is a critical factor negatively impacting the performance of LLM agents. Notably, as task complexity increases, the behavior gap widens (correlation: 0.963), leading to a degradation of agent performance on complex task-oriented dialogs. For the most complex task in our study, even the GPT-4o-based agent exhibits low alignment with human behavior, with low F1 scores for dialog acts (0.464), excessive and often misaligned tool usage with a F1 score of 0.139, and ineffective usage of external knowledge. Reducing such behavior gaps leads to significant performance improvement (22.4% on average). This study highlights the importance of comprehensive behavioral evaluations and improved alignment strategies to enhance the effectiveness of LLM-based TODS in handling complex tasks.

The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs

Understanding historical and cultural artifacts demands human expertise and advanced computational techniques, yet the process remains complex and time-intensive. While large multimodal models offer promising support, their evaluation and improvement require a standardized benchmark. To address this, we introduce TimeTravel, a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions. Designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries, TimeTravel provides a structured dataset and robust evaluation framework to assess AI models’ capabilities in classification, interpretation, and historical comprehension. By integrating AI with historical research, TimeTravel fosters AI-powered tools for historians, archaeologists, researchers, and cultural tourists to extract valuable insights while ensuring technology contributes meaningfully to historical discovery and cultural heritage preservation. We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement. Our goal is to establish AI as a reliable partner in preserving cultural heritage, ensuring that technological advancements contribute meaningfully to historical discovery.

Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts

Modern IT systems generate vast amounts of log data, which pose challenges for Large Language Models (LLMs) due to their large size, irrelevant entries, and non-Natural Language (non-NL) construct (e.g., domain-specific jargon, error codes, file paths, and abbreviations). Traditional methods like Retrieval-Augmented Generation (RAG) and GraphRAG fail to preserve temporal sequences, handle non-NL for context and entities extraction, and dynamically prioritize query-relevant context. To address these limitations, we propose HG-InsightLog, a novel framework that constructs a multi-entity temporal hypergraph representing log attribute-value pair as nodes and connecting them with hyperedges, capturing critical connections in the data. HG-InsightLog introduces a multi-step query personalization mechanism enhancing the Personalized PageRank algorithm to rank hyperedges based on query relevance and contextual centrality to priortize critical connections. Top ranked hyperedges are extracted and converted back into log formats preserving temporal order and reducing context. Experimental results across multiple datasets demonstrate its superiority over existing methods, enhancing factual, causal, and analytical reasoning. Our approach enables smaller LLMs like LLaMA-8B to perform effective log-based QA. Being model-agnostic and training-free, it scales with evolving open-source LLMs without relying on proprietary systems.

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UniT: One Document, Many Revisions, Too Many Edit Intention Taxonomies

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