Austria

Specialized entity linking (EL) models are well-trained at mapping mentions to unique knowledge base (KB) entities according to a given context.
However, specialized EL models struggle to disambiguate long-tail entities due to their limited training data.
Meanwhile, extensively pre-trained large language models (LLMs) possess broader knowledge of uncommon entities.
Yet, with a lack of specialized EL training, LLMs frequently fail to generate accurate KB entity names, limiting their standalone effectiveness in EL.
With the observation that LLMs are more adept at context generation instead of EL execution, 
we introduce LLM-Augmented Entity Linking (LLMAEL), 
the first framework to enhance specialized EL models with LLM data augmentation.
LLMAEL leverages off-the-shelf, tuning-free LLMs as context augmenters, generating entity descriptions to serve as additional input for specialized EL models.
Experiments show that LLMAEL sets new state-of-the-art results across 6 widely adopted EL benchmarks: compared to prior methods that integrate tuning-free LLMs into EL, LLMAEL achieves an absolute 8.9\% gain in EL accuracy. 
We release our datasets and models to facilitate future research.

ACL 2025

LLMaEL: Large Language Models are Good Context Augmenters for Entity Linking

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

workshop paper

### 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)

Wikipedia, as a massively multilingual, community-driven platform, is a valuable resource for Natural Language Processing (NLP), yet the consistency of honorific usage in honorific-rich languages remains underexplored.
Honorifics, subtle yet profound linguistic markers, encode social hierarchies, politeness norms, and cultural values, but Wikipedia's editorial guidelines lack clear standards for their usage in languages where such forms are grammatically and socially prevalent. 
This paper addresses this gap through a large-scale, analysis of third-person honorific pronouns and verb forms in Hindi and Bengali Wikipedia articles. Using Large Language Models (LLM), we automatically annotate $10,000$ articles per language for honorific usage and socio-demographic features such as gender, age, fame, and cultural origin.
We investigate: (i) the consistency of honorific usage across articles, (ii) how inconsistencies correlate with socio-cultural factors, and (iii) the presence of explicit or implicit biases across languages.
We find that honorific usage is consistently more common in Bengali than Hindi, while non-honorific forms are more frequent for infamous, juvenile, and exotic entities in both. Notably, gender bias emerges in both languages, particularly in Hindi, where men are more likely to receive honorifics than women.
Our analysis highlights the need for Wikipedia to develop language-specific editorial guidelines for honorific usage.

Women, Infamous, and Exotic Beings: What Honorific Usages in Wikipedia Reflect on the Cross-Cultural Sociolinguistic Norms?

Given Wikipedia’s role as a trusted source of high-quality, reliable content, there are growing concerns about the proliferation of low-quality machine-generated text (MGT) produced by large language models (LLMs) on its platform. Reliable detection of MGT is therefore essential, yet existing work primarily evaluates MGT detectors on generic generation tasks, rather than on tasks more commonly performed by Wikipedia editors. This misalignment can lead to poor generalisability when applied to real-world Wikipedia contexts.
We introduce WETBench, a multilingual, multi-generator, and task-specific benchmark for MGT detection. We define three editing tasks empirically grounded in Wikipedia editors’ perceived use cases for LLM-assisted editing: Paragraph Writing, Summarisation, and Text Style Transfer, which we implement using two new datasets across three languages. For each writing task, we evaluate three prompts, produce MGT across multiple generators using the best-performing prompt, and benchmark diverse detectors.
We find that, across settings, training-based detectors achieve an average accuracy of 78%, while zero-shot detectors average 58%. These results demonstrate that detectors struggle with MGT in realistic generation scenarios and underscore the importance of evaluating such models on diverse, task-specific data to assess their reliability in editor-driven contexts.

WETBench: A Benchmark for Detecting Task-Specific Machine-Generated Text on Wikipedia

Wikipedia is powered by MediaWiki, a free and open-source software that is also the infrastructure for many other wiki-based online encyclopedias. These include the recently launched website Ruwiki, which has copied and modified the original Russian Wikipedia content to conform to Russian law. To identify practices and narratives that could be associated with different forms of knowledge manipulation, this article presents an in-depth analysis of this Russian Wikipedia fork. We propose a methodology to characterize the main changes with respect to the original version. The foundation of this study is a comprehensive comparative analysis of more than 1.9M articles from Russian Wikipedia and its fork. Using meta-information and geographical, temporal, categorical, and textual features, we explore the changes made by Ruwiki editors. Furthermore, we present a classification of the main topics of knowledge manipulation in this fork, including a numerical estimation of their scope. This research not only sheds light on significant changes within Ruwiki, but also provides a methodology that could be applied to analyze other Wikipedia forks and similar collaborative projects.

Characterizing Knowledge Manipulation in a Russian Wikipedia Fork

Proper nouns in Arabic Wikipedia are frequently undiacritized, creating ambiguity in pronunciation and interpretation, especially for transliterated named entities of foreign origin. While transliteration and diacritization have been well-studied separately in Arabic NLP, their intersection remains underexplored. In this paper, we introduce a new manually diacritized dataset of Arabic proper nouns of various origins with their English Wikipedia equivalent glosses, and present the challenges and guidelines we followed to create it. We benchmark GPT-4o on the task of recovering full diacritization given the undiacritized Arabic and English forms, and analyze its performance. Achieving 73\% accuracy, our results underscore both the difficulty of the task and the need for improved models and resources. We release our dataset to facilitate further research on Arabic Wikipedia proper noun diacritization.

Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset

Wikipedia serves millions of readers daily with multimedia articles, but its content is not equally accessible to all. Visually impaired readers who rely on screen readers can access only a small fraction of images due to missing alt text.
We explore the potential of multimodal generative AI (GPT-4o) to generate high-quality alt text for Wikipedia images. Guided by accessibility best practices, we design four prompting strategies that incorporate writing guidelines and the textual context surrounding each image.
We evaluate these strategies through crowdsourcing, expert review, and a study involving blind and low-vision (BLV) readers. Crowd worker evaluations show that combining context and guidelines significantly improves alt-text quality, earning six times more majority votes than the baseline. Expert editors confirm these results and, through qualitative analysis, identify recurring issues related to accuracy, relevance, formatting, screen reader compatibility, and inclusive representation.
Interviews with BLV users reveal a consistent preference for context- and criteria-grounded alt text, along with concrete recommendations for improving description quality.
This work offers practical guidance for developing AI-assisted tools to help editors make Wikipedia more accessible.

Can LLMs Improve Image Accessibility on Wikipedia?

Malicious sockpuppet detection on Wikipedia is critical to preserving access to reliable information on the internet and preventing the spread of disinformation. Prior machine learning approaches rely on stylistic and meta-data features, but do not prioritise adaptability to author-specific behaviours. As a result, they struggle to effectively model the behaviour of specific sockpuppet-groups, especially when text data is limited. To address this, we propose the application of meta-learning, a machine learning technique designed to improve performance in data-scarce settings by training models across multiple tasks. Meta-learning optimises a model for rapid adaptation to the writing style of a new sockpuppet-group. Our results show that meta-learning significantly enhances the precision of predictions compared to pre-trained models, marking an advancement in combating sockpuppetry on open editing platforms. We release an updated dataset of sockpuppet investigations to foster future research in both sockpuppetry and meta-learning fields.

Detecting Sockpuppetry on Wikipedia Using Meta-Learning

In this paper, we present a thorough analysis of the impact of Large Language Models (LLMs) on Wikipedia, examining the evolution of Wikipedia through existing data and using simulations to explore potential risks. We begin by analyzing page views and article content to study the recent changes in Wikipedia and assess the impact of LLMs. Subsequently, we evaluate how LLMs affect various Natural Language Processing (NLP) tasks related to Wikipedia, including machine translation and retrieval-augmented generation (RAG). Our findings and simulation results reveal that Wikipedia articles have been affected by LLMs, with an impact of approximately 1\%-2\% in certain categories. If the machine translation benchmark based on Wikipedia is influenced by LLMs, the scores of the models may become inflated, and the comparative results among models could shift. Moreover, the effectiveness of RAG might decrease if the knowledge contaminated by LLMs. While LLMs have not yet fully changed Wikipedia's language and knowledge structures, we believe that our empirical findings signal the need for careful consideration of potential future risks.

Wikipedia in the Era of LLMs: Evolution and Risks

Algorithmic hate speech detection is widely used today. However, biases within these systems can lead to discrimination. This research presents an overview of bias mitigation strategies in the field of hate speech detection. The identified principles are grouped into four categories, based on their operation principles. A novel taxonomy of bias mitigation methods is proposed. The mitigation strategies are characterized based on their key concepts and analyzed in terms of their application stage and their need for knowledge of protected attributes. Additionally, the paper discusses potential combinations of these strategies. This research shifts the focus from identifying present biases to examining the similarities and differences between mitigation strategies, thereby facilitating the exchange, stacking, and ensembling of these strategies in future research.

A Comprehensive Taxonomy of Bias Mitigation Methods for Hate Speech Detection

The detection of sensitive content in large datasets is crucial for ensuring that shared and analysed data is free from harmful material. However, current moderation tools, such as external APIs, suffer from limitations in customisation, accuracy across diverse sensitive categories, and privacy concerns. Additionally, existing datasets and open-source models focus predominantly on toxic language, leaving gaps in detecting other sensitive categories such as substance abuse or self-harm. In this paper, we put forward a unified dataset tailored for social media content moderation across six sensitive categories: conflictual language, profanity,sexually explicit material, drug-related content, self-harm, and spam. By collecting and annotating data with consistent retrieval strategies and guidelines, we address the shortcomings of previous focalised research. Our analysis demonstrates that fine-tuning large language models (LLMs) on this novel dataset yields significant improvements in detection performance compared to open off-the-shelf models such as LLaMA, and even proprietary OpenAI models, which underperform by 10-15% overall. This limitation is even more pronounced on popular moderation APIs, which cannot be easily tailored to specific sensitive content categories, among others.

Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation

Algorithmic content moderation governs online speech on large-scale commercial platforms, often under the guise of neutrality. Yet, it routinely reproduces white, middle-class norms of civility and penalizes marginalized voices for unruly and resistant speech. This paper critiques the prevailing ‘pathological' approach to moderation that prioritizes sanitization over justice. Drawing on Marxist-feminist ethics, this paper advances three theses for the future of context-aware algorithmic moderation: (1) prioritizing participatory parity over civility, (2) incorporating identity- and context-aware analysis of speech; and (3) replacing purely numerical evaluations with justice-oriented, community-sensitive metrics. While acknowledging the structural limitations posed by platform capitalism, this paper positions the proposed framework as both critique and provocation, guiding regulatory reform, civil advocacy, and visions for mission-driven online content moderation serving digital commons.

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Women, Infamous, and Exotic Beings: What Honorific Usages in Wikipedia Reflect on the Cross-Cultural Sociolinguistic Norms?

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