Image incomplète : Une étude d’état de l’art sur les biais dans les grands modèles de langage (2025)

Abstract: Les grands modèles de langage (LLM) pré-entraînés ont transformé le traitement du langage naturel (TALN) et les tâches quotidiennes, surpassant les méthodes traditionnelles. Leur utilisation a démocratisé l’accès, facilitant l’écriture, le codage et les conseils de santé. Entraînés sur d’immenses corpus textuels issus d’internet, les LLM héritent de biais, perpétuant des stéréotypes qui peuvent fausser les représentations linguistiques et causer des préjudices représentationnels ou allocationnels. Dans le domaine médical, où les LLM soutiennent la communication et la documentation, ces biais présentent des risques significatifs. Cette revue analyse les recherches sur les biais des LLM, identifie les lacunes concernant les déterminants sociaux de la santé (DSS) et discute de la nécessité d’un cadre pour les aborder de manière exhaustive, améliorant l’intégration sécurisée des LLM en santé.

## introduction since the introduction of chatgpt in 2022 @xcite , pretrained large language models (llms) are increasingly preferred as a choice for natural language processing (nlp) tasks and general uses. these models, built on vast datasets of human-generated text, have consistently outperformed traditio-nal nlp methods across a range of applications, including text classification @xcite , sentiment analysis @xcite , and machine translation @xcite . the introduction of the conversation interface of chatgpt has also increased the accessibility of this new technology to a wider audience, allowing everyone to use llms to help in their everyday tasks, from drafting emails and coding assistance @xcite to seeking advice on personal health concerns @xcite . pretrained llms learn the language patterns from a wide variety of human-created texts available on the internet, therefore, they carry the stereotypes and biases hidden in these texts and propagate the biases in their productions @xcite . these biases can be difficult to detect and evaluate, but they can create "skewed and undesirable association @xcite in language representations which ha @xcite the potential to cause representational or allocational harms" @xcite . in the medical domain, where llms are increasingly explored for applications like conveying medical knowledge, assisting communication with patients, and simplifying documentation tasks @xcite , the stakes of these biases are markedly higher. here, biased outputs could directly or indirectly influence the well-being of real individuals, amplifying existing disparities in healthcare delivery and outcomes. for instance, an llm that disproportionately associates certain diseases with specific demographics based on skewed training data might misguide clinical judgments or patient interactions. several studies have begun to probe these issues @xcite , but they focused primarily on gender and racial biases in llms within medical contexts. a gap in this research lies in its incomplete coverage of social determinants of health (sdoh), the multifaceted conditions that influence an individual's health outcomes, including socioeconomic status, education, occupation, housing, and access to healthcare @xcite . sdoh provide a comprehensive lens through which to understand a patient's lived experience, but existing studies on llm biases tend to focus narrowly on isolated attributes like gender or ethnicity, overlooking how intersecting sdoh might compound representational or allocational harms. for example, focusing on gender bias in llms might miss how this bias can be connected to other determinants such as housing and income level, leading to an incomplete picture of bias in llms in healthcare settings. thus, this review aims to present current research on bias in llms, highlight the gaps, and propose a discussion on developing a comprehensive framework to address more sdoh-related biases, contributing to the efforts to integrate llms into healthcare applications safely. current research on bias in llm can be grouped into several different tasks. gallegos et al. @xcite argued that biasrelated tasks can be grouped into evaluation and mitigation, with the evaluation task comprising the identification of bias in llm through an evaluation corpus and the evaluation of the degree of bias through an evaluation metric. however, ducel et al. @xcite suggested that the task of identifying bias related to llms in downstream tasks is different from the creation of evaluation corpora, so detection and evaluation can be seen as distinct in the body of research. bias in llms can be evaluated using embedding-based, probability-based, or generated text-based metrics, and the different strategies in evaluation metrics are reported in section 2. existing detection methods differ between masked llms and generative llms, and are reported in section 3. for mitigation methods, as bias can enter llms at different stages of the workflow, mitigation methods can be categorized into pre-processing, in-training, intra-processing, and post-processing methods. these methods are further detailed in section 4. section 5 discusses the current research and the need for a framework encompassing a wider range of biases in sdoh. ## evaluation metrics the evaluation metrics for bias in llm can be categorized by what is used from the model to evaluate @xcite . embedding-based metrics typically use the dense vector representation of words or sentences to measure the difference between biased and unbiased responses ; probability-based metrics instead focus on the calculated probabilities to evaluate biases ; and generated text-based metrics look at the output of the model, conditioned by a prompt, to find patterns of biases. ## detection methods bias detection methods are based on using different tasks to uncover stereotypical biases in llms. the most common methods use minimal pairs or question-answer to probe llms for bias. ## mitigation methods detecting and evaluating biases in llms are only the first steps in dealing with biases. mitigation methods are needed to limit the propagation effect of bias in llms. gallegos et al. @xcite proposed a taxonomy of mitigation techniques based on the workflow : pre-processing, in-training, intraprocessing, and post-processing. ## discussion and conclusion despite a body of research on the subject of bias in llms, there exist certain gaps that were raised @xcite @xcite . these studies identify four key limitations in the current literature.