Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study
Notice bibliographique
Résumé
Background: Generative artificial intelligence (AI) is increasingly used to find information. Providing accurate information is essential to support patients with cancer and their families; however, information returned by generative AIs is sometimes wrong. Returning wrong information is called hallucination. Retrieval-augmented generation (RAG), which supplements large language model (LLM) outputs with relevant external sources, has the potential to reduce hallucinations. Although RAG has been proposed as a promising technique, its real-world performance in public health communication remains underexplored. Objective: This study aimed to examine cancer information returned by generative AIs with RAG using cancer-specific information sources and general internet searches to determine whether using RAG with reliable information sources reduces the hallucination rates of generative AI chatbots. Methods: We developed 6 types of chatbots by combining 3 patterns of reference information with 2 versions of LLMs. Thus, GPT-4 and GPT-3.5 chatbots that use cancer information service (CIS) information, Google information, and no reference information (conventional chatbots) were developed. A total of 62 cancer-related questions in Japanese were compiled from public sources. All responses were generated automatically and independently reviewed by 2 experienced clinicians. The reviewers assessed the presence of hallucinations, defined as medically harmful or misinformation. We compared hallucination rates across chatbot types and calculated odds ratios (OR) using generalized linear mixed-effects models. Subgroup analyses were also performed based on whether questions were covered by CIS content. Results: For the chatbots that used information from CIS, the hallucination rates were 0% for GPT-4 and 6% for GPT-3.5, whereas those for chatbots that used information from Google were 6% and 10% for GPT-4 and GPT-3.5, respectively. For questions on information that is not issued by CIS, the hallucination rates for Google-based chatbots were 19% for GPT-4 and 35% for GPT-3.5. The hallucination rates for conventional chatbots were approximately 40%. Using reference data from Google searches generated more hallucinations than using CIS data, with an OR of 9.4 (95% CI 1.2-17.5, P<.01); the OR for the conventional chatbot was 16.1 (95% CI 3.7-50.0, P<.001). While conventional chatbots always generated a response, the RAG-based chatbots sometimes declined to answer when information was lacking. The conventional chatbots responded to all questions, but the response rate decreased (36% to 81%) for RAG-based chatbots. For questions on information not covered by CIS, the CIS chatbots did not respond, while the Google chatbots generated responses in 52% of the cases for GPT-4 and 71% for GPT-3.5. Conclusions: Using RAG with reliable information sources significantly reduces the hallucination rate of generative AI chatbots and increases the ability to admit lack of information, making them more suitable for general use, where users need to be provided with accurate information.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».