Austria

Authorship verification (AV) is a crucial task for applications like identity verification, plagiarism detection, and AI-generated text identification. However, datasets for training and evaluating AV models are primarily in English and primarily in a single domain. This precludes analysis of AV techniques for generalizability and can cause seemingly valid AV solutions to, in fact, rely on topic-based features rather than actual authorship features. To address this limitation, we introduce the Million Authors Corpus (), a novel dataset encompassing contributions from dozens of languages on Wikipedia. It includes only long and contiguous textual chunks taken from Wikipedia edits and links those texts to their authors. includes 60.08M textual chunks, contributed by 1.29M Wikipedia authors. It enables broad-scale cross-lingual and cross-domain AV evaluation to ensure accurate analysis of model capabilities that are not overly optimistic. We provide baseline evaluations using state-of-the-art AV models as well as information retrieval models that are not AV-specific in order to demonstrate &#39;s unique cross-lingual and cross-domain ablation capabilities.

ACL 2025

The Million Authors Corpus: A Cross-Lingual and Cross-Domain Wikipedia Dataset for Authorship Verification

authorship verification

cross-domain

cross-lingual

wikipedia

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Authorship verification (AV) is a crucial task for applications like identity verification, plagiarism detection, and AI-generated text identification. However, datasets for training and evaluating AV models are primarily in English and primarily in a single domain. This precludes analysis of AV techniques for generalizability and can cause seemingly valid AV solutions to, in fact, rely on topic-based features rather than actual authorship features. To address this limitation, we introduce the Million Authors Corpus (), a novel dataset encompassing contributions from dozens of languages on Wikipedia. It includes only long and contiguous textual chunks taken from Wikipedia edits and links those texts to their authors. includes 60.08M textual chunks, contributed by 1.29M Wikipedia authors. It enables broad-scale cross-lingual and cross-domain AV evaluation to ensure accurate analysis of model capabilities that are not overly optimistic. We provide baseline evaluations using state-of-the-art AV models as well as information retrieval models that are not AV-specific in order to demonstrate 's unique cross-lingual and cross-domain ablation capabilities.

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

Ensuring persona fidelity in large language models (LLMs) is essential for maintaining coherent and engaging human-AI interactions. However, LLMs often exhibit Out-of-Character (OOC) behavior, where generated responses deviate from an assigned persona, leading to inconsistencies that affect model reliability. Existing evaluation methods typically assign single scores to entire responses, struggling to capture subtle persona misalignment, particularly in long-form text generation. To address this limitation, we propose an atomic-level evaluation framework that quantifies persona fidelity at a finer granularity. Our three key metrics measure the degree of persona alignment and consistency within and across generations. Our approach enables a more precise and realistic assessment of persona fidelity by identifying subtle deviations that real users would encounter. Through our experiments, we demonstrate that our framework effectively detects persona inconsistencies that prior methods overlook. By analyzing persona fidelity across diverse tasks and personality types, we reveal how task structure and persona desirability influence model adaptability, highlighting challenges in maintaining consistent persona expression.

Spotting Out-of-Character Behavior: Atomic-Level Evaluation of Persona Fidelity in Open-Ended Generation

Using LLMs for Multi-Document Topic Extraction has recently gained popularity due to their apparent high-quality outputs, expressiveness, and ease of use. However, most existing evaluation practices are not designed for LLM-generated topics and result in low inter-annotator agreement scores, hindering the reliable use of LLMs for the task. To address this, we introduce T5Score, an evaluation methodology that decomposes the quality of a topic set into quantifiable aspects, measurable through easy-to-perform annotation tasks. This framing enables a convenient, manual or automatic, evaluation procedure resulting in a strong inter-annotator agreement score.To substantiate our methodology and claims, we perform extensive experimentation on multiple datasets and report the results.

T5Score: A Methodology for Automatically Assessing the Quality of LLM Generated Multi-Document Topic Sets

Large Language Models (LLMs) exhibit remarkable multilingual generalization despite being predominantly trained on English-centric corpora. A fundamental question arises: how do LLMs achieve such robust multilingual capabilities? We take the case of non-Roman script languages, we investigate the role of Romanization—the representation of non-Roman scripts using Roman characters—as a bridge in multilingual processing. Using mechanistic interpretability techniques, we analyze next-token generation and find that intermediate layers frequently represent target words in Romanized form before transitioning to native script, a phenomenon we term Latent Romanization. Further, through activation patching experiments, we demonstrate that LLMs encode semantic concepts similarly across native and Romanized scripts, suggesting a shared underlying representation. Additionally, for translation into non-Roman script languages, our findings reveal that when the target language is in Romanized form, its representations emerge earlier in the model’s layers compared to native script. These insights contribute to a deeper understanding of multilingual representation in LLMs and highlight the implicit role of Romanization in facilitating language transfer.

RomanLens: The Role Of Latent Romanization In Multilinguality In LLMs

We introduce LLM-as-an-Interviewer, a novel paradigm for evaluating large language models (LLMs). This approach leverages multi-turn interactions where the LLM interviewer actively provides feedback on responses and poses follow-up questions to the evaluated LLM. At the start of the interview, the LLM interviewer dynamically modifies datasets to generate initial questions, mitigating data contamination. We apply the LLM-as-an-Interviewer framework to evaluate six models on the reasoning, factuality and instruction-following tasks. Our results show that the framework effectively provides insights into LLM performance, including the quality of initial responses, adaptability to feedback, and ability to address follow-up queries like clarification or additional knowledge requests. The framework also addresses key limitations of conventional methods like LLM-as-a-Judge, including verbosity bias and inconsistency across runs. Finally, we propose the Interview Report, which aggregates insights from the interview process, providing examples and a comprehensive analysis of the LLM’s strengths and weaknesses. This report offers a detailed snapshot of the model's real-world applicability.

LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation

Regulatory compliance is a cornerstone of trust and accountability in critical sectors like finance, healthcare, and technology, yet its complexity poses significant challenges for organizations worldwide. Recent advances in natural language processing, particularly large language models, have demonstrated remarkable capabilities in text analysis and reasoning, offering promising solutions for automating compliance processes. This survey examines the current state of automated compliance, analyzing key challenges and approaches across problem areas. We identify critical limitations in current automated compliance datasets and techniques, including issues of adaptability, completeness, and trust. Looking ahead, we propose research directions to address these challenges, emphasizing standardized evaluation frameworks and balanced human-AI collaboration.

From Complexity to Clarity: AI/NLP's Role in Regulatory Compliance

Research shows that two evaluation experiments reporting results for the same quality criterion name (e.g. Fluency) do not necessarily evaluate the same aspect of quality. Not knowing when two evaluations are comparable in this sense means we currently lack the ability to draw conclusions based on multiple independently conducted evaluations. It is hard to see how this issue can be fully addressed other than by the creation of a standard set of quality criterion names and definitions that the evaluations in use in NLP can be grounded in. Taking a descriptivist approach, the QCET Quality Criteria for Evaluation Taxonomy derives a standard set of quality criterion names and definitions from two surveys of evaluations in NLP, and structures them into a reference taxonomy. We present QCET and its uses in (i) establishing comparability of existing evalua- tions, (ii) guiding the design of new evaluations, and (iii) assessing regulation compliance.

Standard Quality Criteria Derived from Current NLP Evaluations for Guiding Evaluation Design and Grounding Comparability and AI Compliance Assessments

This paper introduces MMRefine, a Multi-Modal Refinement Benchmark designed to evaluate the error refinement capabilities of Multimodal Large Language Models (MLLMs). As the emphasis shifts toward enhancing reasoning during inference, MMRefine provides a framework that assesses refinement beyond final answer accuracy by categorizing errors into diverse levels. The benchmark evaluates MLLMs' abilities to detect and correct errors across six distinct scenarios. Experiments with various open and closed MLLMs reveal refinement bottlenecks and factors impeding performance, highlighting areas for improvement in effective reasoning enhancement.

MMRefine: Unveiling the Obstacles to Robust Refinement in Multimodal Large Language Models

Several studies have explored the mechanisms of large language models (LLMs) in coding tasks, but most have focused on programming languages (PLs) in a monolingual setting. In this paper, we investigate the relationship between multiple PLs and English in the concept space of LLMs. We perform a few-shot translation task on 21 PL pairs using two Llama-based models. By decoding the embeddings of intermediate layers during this task, we observe that the concept space is closer to English (including PL keywords) and assigns higher probabilities to English tokens in the second half of the middle layers, but not in the top layers. We analyze neuron activations for 11 PLs and English, finding that while language-specific neurons are primarily concentrated in the bottom layers, those exclusive to each PL tend to appear in the top layers. For PLs that are highly aligned with multiple other PLs, identifying language-specific neurons is not feasible. These PLs also tend to have a larger keyword set than other PLs and are closer to the model's concept space regardless of the input/output PL in translation task. Our findings provide insights into how LLMs internally represent PLs, revealing structural patterns in the model's concept space. Code is available at [anon. URL].

How Programming Concepts and Neurons Are Shared in Code Language Models

The rapidly changing nature of real-world information presents challenges for large language models (LLMs), which are typically trained on static datasets. This limitation makes it difficult for LLMs to accurately perform tasks that require up-to-date knowledge, such as time-sensitive question answering (QA). In this paper, we introduce **DynaQuest**, a **Dyna**mic **Quest**ion answering dataset reflecting knowledge updates in the real world. DynaQuest is based on Wikipedia Infoboxes, which are frequently updated to reflect real-world changes. Our dataset is created by automatically identifying and comparing changes between different versions of Wikipedia pages and generating question-answer pairs based on these updates. To address the challenges posed by our dynamic dataset, we propose **CARL**, a **C**ontext-**A**ware **R**einforcement **L**earning framework to improve the performance of LLMs on time-sensitive question answering. We conduct experiments on our collected dataset across recent time periods and demonstrate the effectiveness of our approach. Furthermore, we maintain a dynamic knowledge updating process, providing a periodically evolving benchmark to continually evaluate LLMs' ability to answer time-sensitive questions.

DynaQuest: A Dynamic Question Answering Dataset Reflecting Real-World Knowledge Updates

In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. Previously, their context window size imposed a limit on the number of examples that can be shown, making example selection techniques crucial for identifying the maximally effective set of examples. However, the recent advent of Long Context Language Models (LCLMs) has significantly increased the number of examples that can be included in context, raising an important question of whether ICL performance in a many-shot regime is still sensitive to the method of sample selection. To answer this, we revisit these approaches in the context of LCLMs through extensive experiments on 18 datasets spanning 4 tasks. Surprisingly, we observe that sophisticated example selection techniques do not yield significant improvements over a simple random sample selection method. Instead, we discover that the advent of LCLMs has fundamentally shifted the challenge of ICL from that of selecting the most effective examples to that of collecting sufficient examples to fill the context window. Specifically, in certain datasets, including all available examples does not fully utilize the context window; however, by augmenting the examples in context with a simple data augmentation approach, we substantially improve ICL performance by 5%.

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Spotting Out-of-Character Behavior: Atomic-Level Evaluation of Persona Fidelity in Open-Ended Generation

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