Detecting and Reducing Hallucinations in Factual Question Answering
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Large language models (LLMs) have shown remarkable capabilities in factual question answering in recent years, but they sometimes give the answer with hallucinations — responses that seem to be the correct answer but in fact incorrect. Hallucinations give significant challenges for deploying LLMs in reliable medical knowledge queries, which require a high degree of confidence in the resulting output. This study followed the ideas from the paper “On Early Detection of Hallucinations in Factual Question Answering”.
A multi-turn recursive framework for detecting and mitigating hallucinations in large language models (LLM) responses to factual questions was implemented using the Open-llama-7B model. The pipeline leverages a trained classification models guide a language model in its recursive generation. To reduce semantic entropy in the resulting output, a sensitivity hint prefix prompting method was adopted to reduce the hallucination rate in the TriviaQA dataset.
Overall, a recursive generation pipeline with a known prior of whether an answer is hallucinating or not shows promising results. The next phase of the project involves adapting the same architecture for medical Q&A datasets for improved generative response of medical LLMs.
May 1, 2025
