Austria

Claim verification is a critical task in natural language processing (NLP) that involves determining whether available evidence supports or refutes a given claim. While Large Language Models (LLMs) have shown promise in this domain, they still face significant challenges, especially when handling domain-specific knowledge. We propose to enhance LLM-based claim verification through a novel step-by-stepsynthetic data generation framework SynthVerify. Our key insight is that synthetic data generation, guided by domain-specific claim patterns and evidence plans, can effectively address LLMs&#39; knowledge gaps in specialized fields. Using our proposed framework, we create a domain-diverse synthetic claim verification dataset for training zero-shot verification models. We evaluated the fine-tuned LLMs across multiple specialized domains and demonstrate substantial performance gains in verification accuracy compared to the original LLMs, achieving a 20.1 point improvement on the Llama-3-8B model. Our results show that systematically generated synthetic data can effectively address the limitations of current LLM-based verification systems, particularly in handling domain-specific knowledge tasks.

ACL 2025

SYNTHVERIFY: Unleashing the Power of LLMs for Zero-Shot Claim Verification via Step-by-Step Synthetic Data Generation

instruction tuning

synthetic data generation

claim verification

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields 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.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Claim verification is a critical task in natural language processing (NLP) that involves determining whether available evidence supports or refutes a given claim. While Large Language Models (LLMs) have shown promise in this domain, they still face significant challenges, especially when handling domain-specific knowledge. We propose to enhance LLM-based claim verification through a novel step-by-stepsynthetic data generation framework SynthVerify. Our key insight is that synthetic data generation, guided by domain-specific claim patterns and evidence plans, can effectively address LLMs' knowledge gaps in specialized fields. Using our proposed framework, we create a domain-diverse synthetic claim verification dataset for training zero-shot verification models. We evaluated the fine-tuned LLMs across multiple specialized domains and demonstrate substantial performance gains in verification accuracy compared to the original LLMs, achieving a 20.1 point improvement on the Llama-3-8B model. Our results show that systematically generated synthetic data can effectively address the limitations of current LLM-based verification systems, particularly in handling domain-specific knowledge tasks.

poster

### 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)

Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically generate brief answers without reasoning processes and explanations. This limits users’ confidence in making decisions based on their responses. While original CoT shows promise, it lacks self-correction mechanisms during reasoning. This work introduces Domaino1s, which enhances LLMs’ reasoning capabilities on domain tasks through supervised fine-tuning and tree search. We construct CoT-stock-2k and CoT-legal-2k datasets for fine-tuning models that activate domain-specific reasoning steps based on their judgment. Additionally, we propose Selective Tree Exploration to spontaneously explore solution spaces and sample optimal reasoning paths to improve performance. We also introduce PROOF-Score, a new metric for evaluating domain models’ explainability, complementing traditional accuracy metrics with richer assessment dimensions. Extensive experiments on stock investment recommendation and legal reasoning QA tasks demonstrate Domaino1s’s leading performance and explainability. Our code is available at https://anonymous.4open.science/r/Domaino1s-006F/.

Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains

Evaluating generative models with open-ended generation is challenging due to inconsistencies in response formats. Multiple-choice (MC) evaluation mitigates this issue, but generating high-quality distractors is time-consuming and labor-intensive. We introduce D-GEN, the first open-source distractor generator model that transforms open-ended data into an MC format. To evaluate distractor quality, we propose two novel methods: 1) ranking alignment, ensuring generated distractors retain the discriminatory power of ground-truth distractors, and 2) entropy analysis, comparing model confidence distributions. Our results show that D-GEN preserves ranking consistency (Spearman’s 𝜌 0.99, Kendall’s 𝜏 0.94) and closely matches the entropy distribution of ground-truth distractors. Human evaluation further confirms the fluency, coherence, distractiveness, and incorrectness. Our work advances robust and efficient distractor generation with automated evaluation, setting a new standard for MC evaluation.

D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Models

Evaluating the performance of LLMs in multi-turn human-agent interactions presents significant challenges, particularly due to the complexity and variability of user behavior. In this paper, we introduce HammerBench, a novel benchmark framework for assessing LLMs' function-calling capabilities in real-world, multi-turn dialogues. HammerBench simulates diverse mobile assistant use cases, incorporating imperfect instructions, dynamic question-answer trajectories, intent and argument shifts, and the indirect use of external information through pronouns. To construct this benchmark, we curate a comprehensive dataset derived from popular mobile app functionalities and anonymized user logs, complemented by a cost-effective data generation pipeline leveraging open-source models. HammerBench is further augmented with fine-grained interaction snapshots and metrics, enabling detailed evaluation of function-calling performance across individual conversational turns. We demonstrate the effectiveness of HammerBench by evaluating several leading LLMs and uncovering key performance trends. Our experiments reveal that different types of parameter name errors are a significant source of failure across different interaction scenarios, highlighting critical areas for further improvement in LLM robustness for mobile assistant applications.

HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios

Large Language Models have achieved impressive performance across various natural language generation tasks. However, their lack of inherent constraints limits their effectiveness in applications requiring strict adherence to predefined taxonomies, syntactic structures, and domain-specific rules. Existing approaches, such as fine-tuning and prompting, remain insufficient for ensuring compliance with these requirements, particularly in low-resource scenarios and structured text generation tasks.

To address these limitations, we introduce GRAMMAR-LLM, a novel framework that integrates formal grammatical constraints into the LLM decoding process. GRAMMAR-LLM enforces syntactic correctness in linear time while maintaining expressiveness in grammar rule definition. To achieve this, we propose a new class of grammars, which we name LL(prefix), specifically designed for use with LLMs. These grammars are expressive enough to support common tasks such as hierarchical classification, vocabulary restriction, and structured parsing. We formally prove that LL(prefix) grammars can be transformed into LL(1) grammars in linear time, ensuring efficient processing via deterministic pushdown automata. We evaluate GRAMMAR-LLM across diverse NLP tasks, including hierarchical classification, sign language translation, and Abstract Meaning Representation (AMR) parsing. Our experiments, conducted on models such as LLaMA 3 (for classification and translation) and AMRBART (for parsing), demonstrate that GRAMMAR-LLM consistently improves task performance across zero-shot, few-shot, and fine-tuned settings.

Grammar-Constrained Natural Language Generation

This study presents ***BanStereoSet***, a dataset designed to evaluate stereotypical social biases in multilingual LLMs for the Bangla language. In an effort to extend the focus of bias research beyond English-centric datasets, we have localized the content from the StereoSet, IndiBias, and kamruzzaman-etal's datasets, producing a resource tailored to capture biases prevalent within the Bangla-speaking community. Our BanStereoSet dataset consists of 1,194 sentences spanning 9 categories of bias: race, profession, gender, ageism, beauty, beauty in profession, region, caste, and religion. This dataset not only serves as a crucial tool for measuring bias in multilingual LLMs but also facilitates the exploration of stereotypical bias across different social categories, potentially guiding the development of more equitable language technologies in *Bangladeshi* contexts. Our analysis of several language models using this dataset indicates significant biases, reinforcing the necessity for culturally and linguistically adapted datasets to develop more equitable language technologies.

BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Bangla

Multimodal Large Language Models (mLLMs) are trained on a large amount of text-image data. While most mLLMs are trained on caption-like data only, Alayrac et al. (2022) showed that additionally training them on interleaved sequences of text and images can lead to the emergence of in-context learning capabilities. However, the dataset they used, M3W, is not public and is only in English. There have been attempts to reproduce their results but the released datasets are English-only. In contrast, current multilingual and multimodal datasets are either composed of caption-like only or medium-scale or fully private data. This limits mLLM research for the 7,000 other languages spoken in the world. We therefore introduce mOSCAR, to the best of our knowledge the first large-scale multilingual and multimodal document corpus crawled from the web. It covers 163 languages, 303M documents, 200B tokens and 1.15B images. We carefully conduct a set of filtering and evaluation steps to make sure mOSCAR is sufficiently safe, diverse and of good quality. We additionally train two types of multilingual model to prove the benefits of mOSCAR: (1) a model trained on a subset of mOSCAR and captioning data and (2) a model trained on captioning data only. The model additionally trained on mOSCAR shows a strong boost in few-shot learning performance across various multilingual image-text tasks and benchmarks, confirming previous findings for English-only mLLMs. The dataset will be made publicly accessible under the Creative Commons CC BY 4.0 license.

mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus

This paper introduces NorEval, a new and comprehensive evaluation suite for large-scale standardized benchmarking of Norwegian generative language models (LMs). NorEval consists of 24 high-quality human-created datasets – of which five are created from scratch. In contrast to existing benchmarks for Norwegian, NorEval covers a broad spectrum of task categories targeting Norwegian language understanding and generation, establishes human baselines, and focuses on both of the official written standards of the Norwegian language: Bokmål and Nynorsk. All our datasets and a collection of over 100 human-created prompts are integrated into LM Evaluation Harness, ensuring flexible and reproducible evaluation. We describe the NorEval design and present the results of benchmarking 19 open-source pretrained and instruction-tuned LMs for Norwegian in various scenarios. Our benchmark, evaluation framework, and annotation materials will be made publicly upon acceptance.

NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmark

Previous multimodal sentence representation learning methods have achieved impressive performance. However, most approaches focus on aligning images and text at a coarse level, facing two critical challenges: cross-modal misalignment bias and intra-modal semantic divergence, which significantly degrade sentence representation quality. To address these challenges, we propose DALR (Dual-level Alignment Learning for Multimodal Sentence Representation). For cross-modal alignment, we propose a consistency learning module that softens negative samples and utilizes semantic similarity from an auxiliary task to achieve fine-grained cross-modal alignment. Additionally, we contend that sentence relationships go beyond binary positive-negative labels, exhibiting a more intricate ranking structure. To better capture these relationships and enhance representation quality, we integrate ranking distillation with global intra-modal alignment learning. Comprehensive experiments on semantic textual similarity (STS) and transfer (TR) tasks validate the effectiveness of our approach, consistently demonstrating its superiority over state-of-the-art baselines. Our codes are available at https://anonymous.4open.science/r/DALR.

DALR: Dual-level Alignment Learning for Multimodal Sentence Representation Learning

Although multimodal large language models (MLLMs) show promise in generating chart rendering code, editing charts via code presents a greater challenge. This task demands MLLMs to integrate chart understanding and reasoning capacities, which are labor-intensive. While many MLLMs claim such editing capabilities, current evaluations rely on limited case studies, highlighting the urgent need for a comprehensive evaluation framework.In this work, we propose ChartEdit, a new high-quality benchmark designed for chart editing tasks. This benchmark comprises 1,405 diverse editing instructions applied to 233 real-world charts, with each instruction-chart instance having been manually annotated and validated for accuracy. Utilizing ChartEdit, we evaluate the performance of 10 mainstream MLLMs across two types of experiments at both the code and chart levels.The results suggest that large-scale models can generate code to produce images that partially match the reference images.However, their ability to generate accurate edits according to the instructions remains limited. The state-of-the-art (SOTA) model achieves a score of only 59.96, highlighting significant challenges in precise modification. In contrast, small-scale models, including chart-domain models, struggle both with following editing instructions and generating overall chart images, underscoring the need for further development in this area. Code is available at https://github.com/xxlllz/ChartEdit.

ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs’ Capability via Chart Editing

The safety alignment ability of Vision-Language Models (VLMs) is prone to be degraded by the integration of the vision module compared to its LLM backbone. We investigate this phenomenon, dubbed as “safety alignment degradation” in this paper, and show that the challenge arises from the representation gap that emerges when introducing vision modality to VLMs. In particular, we show that the representations of multi-modal inputs shift away from that of text-only inputs which represent the distribution that the LLM backbone is optimized for. At the same time, the safety alignment capabilities, initially developed within the textual embedding space, do not successfully transfer to this new multi-modal representation space. To reduce safety alignment degradation, we introduce Cross-Modality Representation Manipulation (CMRM), an inference time representation intervention method for recovering the safety alignment ability that is inherent in the LLM backbone of VLMs, while simultaneously preserving the functional capabilities of VLMs. The empirical results show that our framework significantly recovers the alignment ability that is inherited from the LLM backbone with minimal impact on the fluency and linguistic capabilities of pre-trained VLMs even without additional training. Specifically, the unsafe rate of LLaVA-7B on multi-modal input can be reduced from 61.53% to as low as 3.15% with only inference-time intervention.

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Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains

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