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arXiv · 2407.12468

Evaluating Search Engines and Large Language Models for Answering Health Questions

Marcos Fernández-Pichel·Juan C. Pichel·David E. Losada
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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 between 50 and 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.

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Marcos Fernández-Pichel, Juan C. Pichel, David E. Losada. 2024-07-17. Evaluating Search Engines and Large Language Models for Answering Health Questions. https://doi.org/10.1038/s41746-025-01546-w

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