RT Book,_Section
T1 Generating Effective Health-Related Queries for Promoting Reliable Search Results
A1 Carrera Alonso, Xiana
A1 Fernández Pichel, Marcos
A1 Losada Carril, David Enrique
K1 Query Variants
K1 Large Language Models
K1 Health Misinformation
AB 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.
PB ACM
SN 979-8-4007-1592-1
YR 2025
FD 2025-07-13
LK https://hdl.handle.net/10347/44795
UL https://hdl.handle.net/10347/44795
LA eng
NO Carrera, X., Fernández Pichel, M. m Losada, D. (2025). Generating Effective Health-Related Queries for Promoting Reliable Search Results. In: SIGIR '25: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. 979-8-4007-1592-1 (pp. 2627-2631)
NO Funded by MICIU/AEI/10.13039/501100011033 (PID2022-137061OB- C22, supported by ERDF) and Xunta de Galicia-Consellería de Cultura, Educación, Formación Profesional e Universidades (ED431G 2023/04, ED431C 2022/19, supported by ERDF).
DS Minerva
RD 7 jun 2026