Austria

Large language models (LLMs) often inherit and amplify social biases embedded in their training data. A prominent social bias is gender bias. In this regard, prior work has mainly focused on gender stereotyping bias – the association of specific roles or traits with a particular gender – in English and on evaluating gender bias in model embeddings or generated outputs. In contrast, gender representation bias – the unequal frequency of references to individuals of different genders – in the training corpora has received less attention. Yet such imbalances in the training data constitute an upstream source of bias that can propagate and intensify throughout the entire model lifecycle. To fill this gap, we propose a novel LLM-based method to detect and quantify gender representation bias in LLM training data in gendered languages, where grammatical gender challenges the applicability of methods developed for English. By leveraging the LLMs&#39; contextual understanding, our approach automatically identifies and classifies person-referencing words in gendered language corpora. Applied to four Spanish-English benchmarks and five Valencian corpora, our method reveals substantial male-dominant imbalances. We show that such biases in training data affect model outputs, but can surprisingly be mitigated leveraging small-scale training on datasets that are biased towards the opposite gender. Our findings highlight the need for corpus-level gender bias analysis in multilingual NLP. We make our code and data publicly available.

ACL 2025

Leveraging Large Language Models to Measure Gender Representation Bias in Gendered Language Corpora

### 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

Large language models (LLMs) often inherit and amplify social biases embedded in their training data. A prominent social bias is gender bias. In this regard, prior work has mainly focused on gender stereotyping bias – the association of specific roles or traits with a particular gender – in English and on evaluating gender bias in model embeddings or generated outputs. In contrast, gender representation bias – the unequal frequency of references to individuals of different genders – in the training corpora has received less attention. Yet such imbalances in the training data constitute an upstream source of bias that can propagate and intensify throughout the entire model lifecycle. To fill this gap, we propose a novel LLM-based method to detect and quantify gender representation bias in LLM training data in gendered languages, where grammatical gender challenges the applicability of methods developed for English. By leveraging the LLMs' contextual understanding, our approach automatically identifies and classifies person-referencing words in gendered language corpora. Applied to four Spanish-English benchmarks and five Valencian corpora, our method reveals substantial male-dominant imbalances. We show that such biases in training data affect model outputs, but can surprisingly be mitigated leveraging small-scale training on datasets that are biased towards the opposite gender. Our findings highlight the need for corpus-level gender bias analysis in multilingual NLP. We make our code and data publicly available.

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)

Large Language Models (LLMs) offer promising alternatives to traditional occupational coding approaches in survey research. Using a German dataset, we examine the extent to which LLM-based occupational coding differs by gender. Our findings reveal systematic disparities: gendered job titles (e.g., “Autor” vs. “Autorin”, meaning “male author” vs. “female author”) frequently result in diverging occupation codes, even when semantically identical. Across all models, 54%–82% of gendered inputs obtain different Top-5 suggestions. The practical impact, however, depends on the model. GPT includes the correct code most often (62%) but demonstrates female bias (up to +18 pp). IBM is less accurate (51%) but largely balanced. Alibaba, Gemini, and MiniLM achieve about 50% correct-code inclusion, and their small (< 10 pp) and direction-flipping gaps could indicate a sampling noise rather than gender bias. We discuss these findings in the context of fairness and reproducibility in NLP applications for social data.

Mind the Gap: Gender-based Differences in Occupational Embeddings

Social bias in language models can potentially exacerbate social inequalities. Despite it having garnered wide attention, most research focuses on English data. In a low-resource scenario, the models often perform worse due to insufficient training data. This study aims to leverage high-resource language corpora to evaluate bias and experiment with debiasing methods in low-resource languages. We evaluated the performance of recent multilingual models in five languages: English (\textsc{eng}), Chinese (\textsc{zho}), Russian (\textsc{rus}), Indonesian (\textsc{ind}) and Thai (\textsc{tha}), and analyzed four bias dimensions: \textit{gender}, \textit{religion}, \textit{nationality}, and \textit{race-color}. By constructing multilingual bias evaluation datasets, this study allows fair comparisons between models across languages. We have further investigated three debiasing methods-\texttt{CDA}, \texttt{Dropout}, \texttt{SenDeb}-and demonstrated that debiasing methods from high-resource languages can be effectively transferred to low-resource ones, providing actionable insights for fairness research in multilingual NLP.

Bias Beyond English: Evaluating Social Bias and Debiasing Methods in a Low-Resource Setting

Understanding the sources of variability in annotations is crucial for developing fair NLP systems, especially for tasks like sexism detection where demographic bias is a concern. This study investigates the extent to which annotator demographic features influence labeling decisions compared to text content. Using a Generalized Linear Mixed Model, we quantify this influence, finding that while statistically present, demographic factors account for a minor fraction (~8%) of the observed variance, with tweet content being the dominant factor. We then assess the reliability of Generative AI (GenAI) models as annotators, specifically evaluating if guiding them with demographic personas improves alignment with human judgments. Our results indicate that simplistic persona prompting often fails to enhance, and sometimes degrades, performance compared to baseline models. Furthermore, explainable AI (XAI) techniques reveal that model predictions rely heavily on content-specific tokens related to sexism, rather than correlates of demographic characteristics. We argue that focusing on content-driven explanations and robust annotation protocols offers a more reliable path towards fairness than potentially persona simulation.

Assessing the Reliability of LLMs Annotations in the Context of Demographic Bias and Model Explanation

This paper presents WoNBias, a curated Bengali dataset to identify gender-based biases, stereotypes, and harmful language directed at women. It merges digital sources- social media, blogs, news- with offline tactics comprising surveys and focus groups, alongside some existing corpora to compile a total of 31,484 entries (10,656 negative; 10,170 positive; 10,658 neutral). WoNBias reflects the sociocultural subtleties of bias in both Bengali digital and offline conversations. By bridging online and offline biased contexts, the dataset supports content moderation, policy interventions, and equitable NLP research for Bengali, a low-resource language critically underserved by existing tools. WoNBias aims to combat systemic gender discrimination against women on digital platforms, empowering researchers and practitioners to combat harmful narratives in Bengali-speaking communities.

WoNBias: A Dataset for Classifying Bias & Prejudice Against Women in Bengali Text

Vision-language models (VLMs) can achieve high accuracy in medical applications but can retain demographic biases from training data. While multiple works have identified the presence of these biases in many VLMs, it remains unclear how strong their impact at the inference level is. In this work, we study how well a task-level explainability method based on linear combinations of words can detect multiple types of biases, with a focus on medical image classification. By manipulating the training datasets with demographic and non-demographic biases, we show how the adopted approach can detect explicitly encoded biases but fails with implicitly encoded ones, particularly biological sex. Our results suggest that such a failure likely stems from misalignment between sex-describing features in image versus text modalities. Our findings highlight limitations in the evaluated explainability method for detecting implicit biases in medical VLMs.

Strengths and Limitations of Word-Based Task Explainability in Vision Language Models: a Case Study on Biological Sex Biases in the Medical Domain

The widespread use of language models, especially Large Language Models, paired with their inherent biases can propagate and amplify societal inequalities. 
While research has extensively explored methods for bias mitigation and measurement, limited attention has been paid to how such biases are communicated to users, which instead can have a positive impact on increasing user trust and understanding of these models. 
Our study addresses this gap by investigating user preferences for gender bias mitigation, measurement and communication in language models. 
To this end, we conducted a user study targeting female AI practitioners with eighteen female and one male participant. 
Our findings reveal that user preferences for bias mitigation and measurement show strong consensus, whereas they vary widely for bias communication, underscoring the importance of tailoring warnings to individual needs.
Building on these findings, we propose a framework for user-centred bias reporting, which leverages runtime monitoring techniques to assess and visualise bias in real time and in a customizable fashion.

Wanted: Personalised Bias Warnings for Gender Bias in Language Models

Within the context of Natural Language Processing (NLP), fairness evaluation is often associated with the assessment of bias and reduction of associated harm. In this regard, the evaluation is usually carried out by using a benchmark dataset, for a task such as Question Answering, created for the measurement of bias in the model's predictions along various dimensions, including gender identity. In our work, we evaluate gender bias in German Large Language Models (LLMs) using the Bias Benchmark for Question Answering by Parrish et al. (2022) as a reference. Specifically, the templates in the gender identity subset of this English dataset were machine translated into German. The errors in the machine translated templates were then manually reviewed and corrected with the help of a language expert. We find that manual revision of the translation is crucial when creating datasets for gender bias evaluation because of the limitations of machine translation from English to a language such as German with grammatical gender. Our final dataset is comprised of two subsets: Subset-I, which consists of group terms related to gender identity, and Subset-II, where group terms are replaced with proper names. We evaluate several LLMs used for German NLP on this newly created dataset and report the accuracy and bias scores. The results show that all models exhibit bias, both along and against existing social stereotypes.

GG-BBQ: German Gender Bias Benchmark for Question Answering

Addressing the critical need for robust bias testing in AI systems, current methods often rely on overly simplistic or rigid persona templates, limiting the depth and realism of fairness evaluations. We introduce a novel framework and an associated tool designed to generate high-quality, diverse, and configurable personas specifically for nuanced bias assessment. Our core innovation lies in a two-stage process: first, generating structured persona tags based solely on user-defined configurations (specified manually or via an included agent tool), ensuring attribute distributions are controlled and crucially, are not skewed by an LLM's inherent biases regarding attribute correlations during the selection phase. Second, transforming these controlled tags into various realistic outputs—including natural language descriptions, CVs, or profiles—suitable for diverse bias testing scenarios. This tag-centric approach preserves ground-truth attributes for analyzing correlations and biases within the generated population and downstream AI applications. We demonstrate the system's efficacy by generating and validating 1,000 personas, analyzing both the adherence of natural language descriptions to the source tags and the potential biases introduced by the LLM during the transformation step. The provided dataset, including both generated personas and their source tags, enables detailed analysis. This work offers a significant step towards more reliable, controllable, and representative fairness testing in AI development.

Tag-First: Mitigating Distributional Bias in Synthetic User Profiles through Controlled Attribute Generation

This paper addresses a bias of gender inference systems: their binary nature. Based on the observation that, for French, systems based on pattern-matching of grammatical gender markers in "I am" expressions perform better than machine-learning approaches (Ciot et al. 2013), we examine the use of grammatical gender by non-binary individuals. We describe the construction of a corpus of texts produced by non-binary authors on Reddit, (formely) Twitter and three forums. Our linguistic analysis shows three main patterns of use: authors who use non-binary markers, authors who consistently use one grammatical gender, and authors who use both feminine and masculine markers. Using this knowledge, we make proposals for the improvements of existing gender inference systems based on grammatical gender.

Characterizing non-binary French: A first step towards debiasing gender inference

Large language models (LLMs) have demonstrated strong zero-shot machine translation (MT) performance but often exhibit gender bias that is present in their training data, especially when translating into grammatically gendered languages. In this paper, we investigate whether explicitly providing gender information can mitigate this issue and improve translation quality. We propose a two-step approach: (1) inferring entity gender from context, and (2) incorporating this information into prompts using either \textbf{Structured Tagging} or \textbf{Natural Language}. Experiments with five LLMs across four language pairs show that explicit gender cues consistently reduce gender errors, with structured tagging yielding the largest gains. Our results highlight prompt-level gender disambiguation as a simple yet effective strategy for more accurate and fair zero-shot MT.

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Mind the Gap: Gender-based Differences in Occupational Embeddings

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Mind the Gap: Gender-based Differences in Occupational Embeddings

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Gender Bias in Large Language Models across Multiple Languages: A Case Study of ChatGPT

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