Summary of Biokgbench: a Knowledge Graph Checking Benchmark Of Ai Agent For Biomedical Science, by Xinna Lin et al.
BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science
by Xinna Lin, Siqi Ma, Junjie Shan, Xiaojing Zhang, Shell Xu Hu, Tiannan Guo, Stan Z. Li, Kaicheng Yu
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper introduces BioKGBench, a novel evaluation benchmark for biomedical agents driven by Large Language Models (LLMs). The benchmark assesses two atomic abilities: “Understanding” unstructured text from research papers and “Literature” grounding through Knowledge-Graph Question-Answering (KGQA). The authors collect data for two tasks and 225 high-quality annotated data for the agent task. Surprisingly, state-of-the-art agents struggle on this benchmark. A simple yet effective baseline, BKGAgent, is introduced, revealing over 90 factual errors in a widely used knowledge graph. This approach demonstrates the importance of precise evaluation in biomedical AI research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper wants to make sure that artificial intelligence (AI) tools are working well for scientists who study diseases and medicine. The authors create a new way to test these AI tools, which they call BioKGBench. It has two parts: understanding scientific texts and using information from databases to answer questions. They find that even the best AI tools don’t do very well on this test. To help, they make a simpler tool called BKGAgent that can still be useful. |
Keywords
» Artificial intelligence » Grounding » Knowledge graph » Question answering
