[Phd 2023] Evaluating stereotyped biases in auto-regressive language models
EVALUATING STEREOTYPED BIASES IN AUTO-REGRESSIVE LANGUAGE MODELS
Université de Lorraine
IAEM – INFORMATIQUE – AUTOMATIQUE – ELECTRONIQUE – ELECTROTECHNIQUE – MATHEMATIQUES
Unité de recherche
LORIA – Laboratoire Lorrain de Recherche en Informatique et ses Applications
Encadrement de la thèse
Université de Lorraine
Début de la thèse
le 1 octobre 2023
Date limite de candidature
(à 23h59) 15 mai 2023
Mots clés – Keywords
The objective of the doctoral research is to provide a fine-grained understanding of biases encoded in auto-regressive language models. Specifically, the PhD candidate will produce resources and tools for the extrinsic evaluation of stereotyped biases and conduct a comprehensive evaluation of language models that encompasses an ethical dimension as well as performance metrics. A first step of the work will consist in building a solid state-of-the-art about stereotyped biases evaluation. This should include all extrinsic methods, including prompt engineering, as well as the existing metrics. In parallel, the PhD candidate will determine if previously created datasets, such as CrowS-Pairs (Nangia2020) and its adaptations in other languages like French CrowS-Pairs (Neveol2022) can be re-used in the context of auto-regressive language models and propose appropriate metrics. Another dimension that we want to cover in the work is to check the consistency of the results obtained on the models’ pre-training applications (eg with CrowS-Pairs) and some more downstream applications. Potential candidates could be NLP applications supporting public health, such as the extraction of epidemiological indicators from clinical narratives, as we have experience on these. All the produced resources and tools will be made available to the entire community on a freely accessible online repository (Inria GitLab).
Large language models have been at the heart of the majority of Natural Language Processing (NLP) tools and applications for the past 4 years now. After the success of the masked language models (eg BERT), auto-regressive language models (eg GPT) are now the most widely used. However, regardless of the architecture and the languages they cover, these models reproduce and amplify the stereotypes which are present in the datasets used to train them (Zhao2017, Jia2020). These stereotyped biases have a strong negative impact on society, especially on the historically most disadvantaged groups (Hovy2016, Bender2021). Many research efforts have focused on mitigating these negative effects, either by improving the documentation of the corpora used for training (Couillault2014, Bender2018, Gebru2021) or by debiaising the models (Meade2022). Other efforts aim at producing specific data allowing to measure the degree of stereotype of the productions (Nangia2020, Neveol2022). However, these experiments mainly addressed the masked language models (like BERT (Devlin2019), not the auto-regressive ones like GPT3 (Brown2020) or Bloom (Scao2022). With the advent of chatGPT, a variant of auto-regressive model using Reinforcement Learning from Human Feedback (RLHF), and the numerous issues uncovered by the users1, the urge for a scientifically sound methodology of evaluation has become obvious. Finally, most research work in bias and fairness in NLP is focused on gender bias in American English (Talat et al. 2022).
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[Scao et al., 2022] Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., et al. (2022). BLOOM : A 176B-Parameter Open-Access Multilingual Language Model. working paper or preprint. [Talat et al., 2022] Zeerak Talat, Aurélie Névéol, Stella Biderman, Miruna Clinciu, Manan Dey, Shayne Longpre, Sasha Luccioni, Maraim Masoud, Margaret Mitchell, Dragomir Radev, Shanya Sharma, Arjun Subramonian, Jaesung Tae, Samson Tan, Deepak Tunuguntla, and Oskar Van Der Wal. 2022. You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings. In Proceedings of BigScience Episode #5 — Workshop on Challenges & Perspectives in Creating Large Language Models, pages 26–41, virtual+Dublin. Association for Computational Linguistics. [Zhao et al., 2017] Zhao, J., Wang, T., Yatskar, M., Ordonez, V., and Chang, K.-W. (2017). Men also like shopping : Reducing gender bias amplification using corpus-level constraints. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2979–2989, Copenhagen, Denmark. Association for Computational Linguistics
Details on the thesis supervision
Aurélie Névéol (co-directrice, DR CNRS, LISN)
Scientific, material and financial conditions of the research project
La recherche aura lieu au LORIA, à Nancy, au sein de l’équipe Sémagramme.
Objectives of the doctoral student’s research work: dissemination, publication and confidentiality, intellectual property rights, etc.
Les travaux de recherche feront l’objet de publications régulières en accès libre. Toutes les données produites seront mises à la disposition de la communauté sous licence CC sur une plateforme en ligne. Le code produit sera également mis à disposition sous licence libre sur une plateforme.
Profile and skills required
MSc in Natural Language Processing.
Interest in ethics for NLP and datasets building.
Languages: Fluent French and very good English.