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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2407.12468 |
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| _version_ | 1866909526171582464 |
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| author | Fernández-Pichel, Marcos Pichel, Juan C. Losada, David E. |
| author_facet | Fernández-Pichel, Marcos Pichel, Juan C. Losada, David E. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12468 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Search Engines and Large Language Models for Answering Health Questions Fernández-Pichel, Marcos Pichel, Juan C. Losada, David E. Information Retrieval Artificial Intelligence 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. |
| title | Evaluating Search Engines and Large Language Models for Answering Health Questions |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2407.12468 |