Summary of Comparing Knowledge Sources For Open-domain Scientific Claim Verification, by Juraj Vladika et al.
Comparing Knowledge Sources for Open-Domain Scientific Claim Verification
by Juraj Vladika, Florian Matthes
First submitted to arxiv on: 5 Feb 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 The proposed open-domain claim verification system efficiently verifies scientific claims by leveraging various knowledge sources and information retrieval techniques. The model’s pipeline is designed to handle large datasets and query millions of documents to find relevant evidence. In this study, the authors test the performance of different systems on four biomedical and health-related datasets, demonstrating that PubMed outperforms other sources for specialized biomedical claims while Wikipedia excels for everyday health concerns. Additionally, the results highlight the strengths of BM25 in retrieval precision and semantic search in recall. The findings provide valuable insights into frequent retrieval patterns and challenges, paving the way for future developments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are working to develop fact-checking systems that can verify scientific claims online. Usually, these systems assume they already have all the information needed, but this isn’t realistic because there could be millions of documents to search through. In this study, researchers tested different ways to find relevant evidence for biomedical and health-related claims. They found that using PubMed is best for specialized medical claims while Wikipedia is better for everyday health concerns. The results show how well each method performs in finding the right information. |
Keywords
» Artificial intelligence » Precision » Recall
