Summary of Attribute Structuring Improves Llm-based Evaluation Of Clinical Text Summaries, by Zelalem Gero et al.
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries
by Zelalem Gero, Chandan Singh, Yiqing Xie, Sheng Zhang, Praveen Subramanian, Paul Vozila, Tristan Naumann, Jianfeng Gao, Hoifung Poon
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a framework for mitigating issues in clinical text summarization, particularly when using Large Language Models (LLMs). The proposed Attribute Structuring (AS) framework structures the evaluation process to improve correspondence between human annotations and automated metrics. AS decomposes the evaluation into simple tasks that an LLM can perform, rather than attempting holistic summary evaluation. The experiments demonstrate that AS consistently improves accuracy and enables efficient human auditing, making it a trustworthy approach for evaluating clinical information in resource-constrained scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping computers summarize medical text better. Right now, machines struggle to make accurate summaries because they might include false or unproven information. The researchers developed a way to fix this problem by breaking down the evaluation process into smaller tasks that are easier for computers to handle. This makes it possible to check if the computer’s summary is correct and trustworthy. The new approach helps ensure that medical information is accurate, which is very important in healthcare. |
Keywords
» Artificial intelligence » Summarization
