Evaluating Search Engines and Large Language Models for Answering Health Questions

• URL: http://arxiv.org/abs/2407.12468v3
• Date: Thu, 06 Mar 2025 11:53:49 GMT
• Title: Evaluating Search Engines and Large Language Models for Answering Health Questions
• Authors: Marcos Fernández-Pichel, Juan C. Pichel, David E. Losada,
• Abstract summary: This study compares the performance of four popular SEs, seven LLMs, and retrieval-augmented (RAG) variants in answering 150 health-related questions.<n>Findings reveal SEs correctly answer between 50 and 70% of questions, often hindered by many retrieval results not responding to the health question.<n>RAG methods significantly enhance smaller LLMs' effectiveness, improving accuracy by up to 30% by integrating retrieval evidence.
• Score: 3.8984586307450093
• License: http://creativecommons.org/licenses/by/4.0/
• 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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