Summary of Mrscore: Evaluating Radiology Report Generation with Llm-based Reward System, by Yunyi Liu et al.
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System
by Yunyi Liu, Zhanyu Wang, Yingshu Li, Xinyu Liang, Lingqiao Liu, Lei Wang, Luping Zhou
First submitted to arxiv on: 27 Apr 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 The paper introduces a novel automatic evaluation metric, MRScore, specifically designed for radiology report generation using Large Language Models (LLMs). Conventional natural language generation (NLG) metrics like BLEU are insufficient for accurately assessing generated reports. The authors collaborated with radiologists to develop a framework guiding LLMs for radiology report evaluation, ensuring alignment with human analysis. The framework involves generating large amounts of training data and pairing reports as accepted or rejected samples. The paper demonstrates MRScore’s higher correlation with human judgments and superior performance in model selection compared to traditional metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to measure how well computers generate radiology reports. Right now, there are problems with the ways we evaluate these reports, so the authors created a special metric called MRScore. They worked with doctors to make sure it accurately reflects what humans look for in good reports. The method involves generating lots of training data and labeling some as “good” and others as “bad.” This helps train computers to produce better reports. The results show that MRScore is more accurate than old methods. |
Keywords
» Artificial intelligence » Alignment » Bleu
