Evaluating search engines and large language models for answering health questions.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 40065094.
- Also identified by DOI 10.1038/s41746-025-01546-w and PMC identifier 11894092.
- Licence recorded as CC BY-NC-ND.
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Abstract
Search engines (SEs) have traditionally been primary tools for information seeking, but the new large language models (LLMs) are emerging as powerful alternatives, particularly for question-answering tasks. This study compares the performance of four popular SEs, seven LLMs, and retrieval-augmented (RAG) variants in answering 150 health-related questions from the TREC Health Misinformation (HM) Track. Results reveal SEs correctly answer 50-70% of questions, often hindered by many retrieval results not responding to the health question. LLMs deliver higher accuracy, correctly answering about 80% of questions, though their performance is sensitive to input prompts. RAG methods significantly enhance smaller LLMs' effectiveness, improving accuracy by up to 30% by integrating retrieval evidence.