Search Engines, LLMs or Both? Evaluating Information Seeking Strategies for Answering Health Questions

• URL: http://arxiv.org/abs/2407.12468v2
• Date: Thu, 18 Jul 2024 10:11:09 GMT
• Title: Search Engines, LLMs or Both? Evaluating Information Seeking Strategies for Answering Health Questions
• Authors: Marcos Fernández-Pichel, Juan C. Pichel, David E. Losada,
• Abstract summary: We compare different web search engines, Large Language Models (LLMs) and retrieval-augmented (RAG) approaches. We observed that the quality of webpages potentially responding to a health question does not decline as we navigate further down the ranked lists. According to our evaluation, web engines are less accurate than LLMs in finding correct answers to health questions.
• Score: 3.8984586307450093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Search engines have traditionally served as primary tools for information seeking. However, the new Large Language Models (LLMs) have recently demonstrated remarkable capabilities in multiple tasks and, specifically, their adoption as question answering systems is becoming increasingly prevalent. It is expected that LLM-based conversational systems and traditional web engines will continue to coexist in the future, supporting end users in various ways. But there is a need for more scientific research on the effectiveness of both types of systems in facilitating accurate information seeking. In this study, we focus on their merits in answering health questions. We conducted an extensive study comparing different web search engines, LLMs and retrieval-augmented (RAG) approaches. Our research reveals intriguing conclusions. For example, we observed that the quality of webpages potentially responding to a health question does not decline as we navigate further down the ranked lists. However, according to our evaluation, web engines are less accurate than LLMs in finding correct answers to health questions. On the other hand, LLMs are quite sensitive to the input prompts, and we also found out that RAG leads to highly effective information seeking methods.

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