La confiance de Mistral-7 B est-elle justifiée ? Une évaluation en auto-estimation pour les questions biomédicales (2025)

Abstract: Évaluer la fiabilité des grands modèles de langage (LLMs) dans des tâches de question-réponse biomédicale est essentiel pour leur déploiement en toute sécurité dans des contextes médicaux. Dans cette étude, nous examinons si Mistral-7B est capable d’estimer avec précision la confiance qu’il accorde à ses propres réponses, en comparant ses scores de similarité auto-attribués à la similarité- cosinus avec des réponses de référence. Nos résultats montrent que Mistral-7B présente une forte tendance à la surconfiance, attribuant systématiquement des scores de similarité élevés, même lorsque la qualité des réponses varie. L’introduction de la génération augmentée par récupération (RAG) améliore la précision des réponses, comme en témoignent les valeurs plus élevées de similarité- cosinus, mais n’améliore pas significativement la calibration de la confiance. Bien que RAG réduise la surconfiance et améliore la corrélation entre les scores de similarité prédits et réels, le modèle continue de surestimer systématiquement la qualité de ses réponses. Ces résultats soulignent la nécessité de mécanismes d’estimation de confiance plus efficaces, afin d’aligner les auto-évaluations du modèle sur la précision réelle de ses réponses. Notre étude montre l’importance d’affiner les techniques de calibration des LLMs pour renforcer leur fiabilité dans les applications biomédicales.

## introduction large language models (llms) are increasingly being used in biomedical applications, where accuracy and reliability are critical. however, a major challenge in deploying these models is their occasional tendency to generate incorrect or misleading information, which can pose significant health risks. given this challenge, the goal is not to replace human decision-making, but rather to provide a second opinion or decision support. to ensure the safe and effective use of llms in such high-stakes domains, it is essential to evaluate not only the quality of their responses but also the reliability of the model's confidence in its own outputs. one possible approach is to have llms provide a confidence estimate alongside their responses, allowing users to trust the reliability of the information provided. however, a fundamental question arises: can we trust the confidence estimates generated by these models? if an llm is overconfident, users may place excessive trust in incorrect answers, leading to potentially harmful medical decisions. conversely, if a model systematically underestimates its confidence, it becomes difficult to distinguish between reliable and unreliable responses, limiting its practical usefulness. simply biasing the model toward more conservative estimates is not a viable solution either, as it would blur the distinction between correct and incorrect answers. to investigate this issue, we assess the ability of mistral-7b foot_0 to provide reliable confidence estimates. specifically, we analyze whether its self-reported confidence scores align with actual answer quality by measuring the cosine similarity. additionally, we explore the potential of retrieval-augmented generation (rag) to improve answer quality and mitigate overconfidence in llms. rag enhances a model's responses by retrieving relevant external information before generating an answer, helping to ground its outputs in factual data. this method has been shown to reduce hallucinations where the model generates false information by integrating evidence-based information @xcite . given this, we hypothesize that rag could also improve confidence calibration by reducing the model's reliance on uncertain internal knowledge. in our setup, the model is presented with additional information retrieved through semantic search, without being explicitly told that it comes from an external source or is directly related to the answer. by analyzing the impact of this implicit exposure to high-quality information, we assess whether this approach leads to improved answer accuracy and better-aligned confidence estimates. we aim to explore whether llms can provide reliable confidence estimates in biomedical questionanswering (qa) and how rag influences their accuracy. our main contributions are listed above. ## related works confidence estimation and calibration in llms the estimation of confidence in llms for qa has been widely studied, with a focus on quantifying uncertainty and improving reliability. a well-calibrated model should not only provide high discrimination, i.e., assigning higher confidence to correct responses than incorrect ones, but also exhibit proper calibration, meaning that its confidence scores accurately reflect the true probability of correctness @xcite @xcite . recent work has shown that llms often struggle with calibration, typically displaying overconfidence, particularly in complex domains like biomedicine. for example, models can assign confidence levels of 90% to responses that are only correct 70-80% of the time, conducting to misleading trust in their output. this issue is critical in sensitive applications, such as medical decision making, where overconfidence can result in unsafe recommendations. studies have explored various uncertainty quantification techniques, including token-level probabilities, confidence elicitation, and sample consistency measures @xcite . other recent analyses of llm confidence metrics highlight that while these models can exhibit strong discrimination, their calibration remains unreliable, particularly in automated evaluation tasks @xcite . a 2023 survey @xcite offers a comprehensive overview of early efforts in confidence estimation and calibration for llms. it identifies key challenges specific to language generation-such as the vast and semantically variable output space-and organizes existing approaches across generative and classification settings. while more recent studies have built on this foundation, the survey remains a key reference for understanding the conceptual and technical landscape of the field. in parallel, the truthfulqa benchmark @xcite addresses the issue of factual correctness from a different angle, by evaluating whether llms generate truthful answers to questions that often elicit misconceptions. the study finds that larger models are more prone to imitative falsehoods-plausible but incorrect answers learned from human text-exposing a counterintuitive "inverse scaling" effect. these findings highlight that increasing model size alone is not sufficient for improving truthfulness, and emphasize the need for evaluation frameworks that go beyond surface-level fluency. a related direction explores whether models can introspect about their own knowledge by predicting their own probability of providing a correct answer (pptrueq), or estimating in advance whether they are likely to know the answer to a question (p(ik), ik stands for "i know"). recent work shows that larger models exhibit strong calibration on diverse question formats, and that self-evaluation improves when models generate multiple answer candidates before scoring their plausibility. furthermore, models trained to estimate p(ik) can anticipate whether they are likely to answer a question correctly-generalizing partially across tasks and improving when contextual information (e.g., hints or source documents) is available. these techniques suggest a promising route for developing more honest and self-aware systems, going beyond mere imitation of training data @xcite . ## conclusion our study evaluates mistral-7b's ability to assess the quality of its own answers by comparing its self-assigned similarity scores to an objective measure. our findings reveal that rag enhances the quality of the model's responses. however, the model tends to be overconfident in its predictions, with this effect increasing when additional context from rag is provided. despite this overconfidence, there is a meaningful correlation between the model's predicted similarity scores and cosine similarity, suggesting that its confidence scores follow a consistent pattern rather than being assigned randomly. these insights contribute to understanding mistral-7b's self-assessment capabilities and highlight areas for improving confidence calibration. it is important to note, however, that these results are specific to the prompt used and to the mistral-7b model. the scope of our experiments was limited due to resource constraints, which means further investigation is needed to generalize these findings across other prompts, models, or tasks.