Summary of Kgarevion: An Ai Agent For Knowledge-intensive Biomedical Qa, by Xiaorui Su et al.
KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA
by Xiaorui Su, Yibo Wang, Shanghua Gao, Xiaolong Liu, Valentina Giunchiglia, Djork-Arné Clevert, Marinka Zitnik
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 research paper introduces KGARevion, a knowledge graph-based agent that answers complex medical questions. The model leverages large language models to generate relevant triplets and verifies them against a grounded knowledge graph to ensure accuracy. This multi-step process strengthens reasoning and adapts to different models of medical inference, outperforming other approaches. Evaluations on medical QA benchmarks show an improvement in accuracy by over 5.2% compared to 15 other models. The agent also demonstrated strong zero-shot generalization to underrepresented medical contexts, including the AfriMed-QA dataset focused on African healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a new way to answer complex medical questions using artificial intelligence (AI). The AI model, called KGARevion, can understand and use large amounts of information from many different sources. It checks its answers against what it knows to be true, which helps make sure the answer is correct. This makes KGARevion better at answering difficult medical questions than other approaches. The researchers tested KGARevion on several datasets and found that it did a great job, especially when answering questions about African healthcare. |
Keywords
» Artificial intelligence » Generalization » Inference » Knowledge graph » Zero shot
