Summary of Improving Health Question Answering with Reliable and Time-aware Evidence Retrieval, by Juraj Vladika et al.
Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval
by Juraj Vladika, Florian Matthes
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles open-domain question answering (QA) in healthcare, a crucial task given the widespread online searching of health-related information. The study focuses on PubMed, a trusted medical research document repository, and employs a retrieve-then-read QA pipeline. The authors explore various retrieval settings to optimize performance, including adjusting the number of retrieved documents, publication year, and citation count. Their results show that reducing the amount of retrieved documents and favoring recent, highly cited articles can boost the macro F1 score by up to 10%. This work highlights the importance of evidence selection in open-domain QA and paves the way for future research on managing evidence disagreement and generating user-friendly explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Have you ever searched online for answers about your health? Existing systems have limitations when it comes to answering new or unusual questions. This study tries to improve this by looking at how we find relevant information in large databases of medical research papers. The researchers use a system that first finds relevant documents and then reads them to answer the question. They experimented with different ways of finding these documents, like using more recent or highly cited articles. Their results show that finding less but more relevant documents can lead to better answers. This study is important because it helps us understand how we can make searching for health information online easier and more accurate. |
Keywords
» Artificial intelligence » F1 score » Question answering
