[2511.06668] Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare

Computer Science > Information Retrieval arXiv:2511.06668 (cs) [Submitted on 10 Nov 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare Authors:Saeedeh Javadi, Sara Mirabi, Manan Gangar, Bahadorreza Ofoghi View a PDF of the paper titled Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare, by Saeedeh Javadi and 3 other authors View PDF HTML (experimental) Abstract:In high-stakes information domains such as healthcare, where large language models (LLMs) can produce hallucinations or misinformation, retrieval-augmented generation (RAG) has been proposed as a mitigation strategy, grounding model outputs in external, domain-specific documents. Yet, this approach can introduce errors when source documents contain outdated or contradictory information. This work investigates the performance of five LLMs in generating RAG-based responses to medicine-related queries. Our contributions are three-fold: i) the creation of a benchmark dataset using consumer medicine information documents from the Australian Therapeutic Goods Administration (TGA), where headings are repurposed as natural language questions, ii) the retrieval of PubMed abstracts using TGA headings, stratified across multiple publication years, to enable controlled temporal evaluation of outdated evidence, and iii) a comparative analysis of the frequency and impact of outdated or contradictory...