A three-stage RAG pipeline generates a cited summary (GenText) from PubMed Central passages and ranks health documents by topical relevance plus alignment with that summary, outperforming baselines on CLEF eHealth and TREC Health Misinformation 2020.
UPV at TREC Health Misinformation Track 2021 Ranking with SBERT and Quality Estimators
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abstract
Health misinformation on search engines is a significant problem that could negatively affect individuals or public health. To mitigate the problem, TREC organizes a health misinformation track. This paper presents our submissions to this track. We use a BM25 and a domain-specific semantic search engine for retrieving initial documents. Later, we examine a health news schema for quality assessment and apply it to re-rank documents. We merge the scores from the different components by using reciprocal rank fusion. Finally, we discuss the results and conclude with future works.
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Enhancing Health Information Retrieval with RAG by Prioritizing Topical Relevance and Factual Accuracy
A three-stage RAG pipeline generates a cited summary (GenText) from PubMed Central passages and ranks health documents by topical relevance plus alignment with that summary, outperforming baselines on CLEF eHealth and TREC Health Misinformation 2020.