Generating Effective Health-Related Queries for Promoting Reliable Search Results

Misinformation on the Internet poses significant risks to users seeking health information. This paper addresses the challenge of generating effective health-related queries to promote reliable search results. We propose a method leveraging Large Language Models to generate synthetic narratives that guide the creation of alternative queries. These queries are designed to retrieve more helpful and fewer harmful documents compared to those retrieved by the original user queries. We evaluate the effectiveness of these queries using classic and neural retrieval models across multiple datasets, demonstrating promising improvements in retrieving reputable content.

Palabras clave: Query Variants, Large Language Models, Health Misinformation