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Aurélie Névéol

9 accepted papers

2025

SHADES: Towards a Multilingual Assessment of Stereotypes in Large Language Models

NAACL 2025long

Large Language Models (LLMs) reproduce and exacerbate the social biases present in their training data, and resources to quantify this issue are limited. While research has attempted to identify and mitigate such biases, most efforts have been concentrated around English, lagging the rapid advanceme…

Cited by 1SourcePDFScholar
2025

“Women do not have heart attacks!” Gender Biases in Automatically Generated Clinical Cases in French

NAACL 2025findings

Healthcare professionals are increasingly including Language Models (LMs) in clinical practice. However, LMs have been shown to exhibit and amplify stereotypical biases that can cause life-threatening harm in a medical context. This study aims to evaluate gender biases in automatically generated cli…

2024

A Benchmark Evaluation of Clinical Named Entity Recognition in French

COLING 2024main

Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora…

2024

A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages

COLING 2024main

User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilin…

2024

Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting

EMNLP 2024finding

Large language models (LLMs) have become the preferred solution for many natural language processing tasks. In low-resource environments such as specialized domains, their few-shot capabilities are expected to deliver high performance. Named Entity Recognition (NER) is a critical task in information…

2024

Limitations of Human Identification of Automatically Generated Text

COLING 2024main

Neural text generation is receiving broad attention with the publication of new tools such as ChatGPT. The main reason for that is that the achieved quality of the generated text may be attributed to a human writer by the naked eye of a human evaluator. In this paper, we propose a new corpus in Fren…

2024

Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts

COLING 2024main

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting The study of bias, fairness and social impact in Natural Language Processing (NLP) lacks resources in languages other than English. Our objective is to support the evaluation of bias in language models i…

Cited by 8SourcePDFScholar
2023

The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research

ACL 2023long

Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As one of the big players in the field of NLP, together with governments and universities, it is important to track the infl…

2022

French CrowS-Pairs: Extending a challenge dataset for measuring social bias in masked language models to a language other than English

ACL 2022long

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting. Much work on biases in natural language processing has addressed biases linked to the social and cultural experience of English speaking individuals in the United States. We seek to widen the scope of b…

Cited by 88SourcePDFScholar