Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917707200331776 |
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| author | Yang, Ziyu Cherian, Santhosh Vucetic, Slobodan |
| author_facet | Yang, Ziyu Cherian, Santhosh Vucetic, Slobodan |
| contents | Radiology reports are highly technical documents aimed primarily at doctor-doctor communication. There has been an increasing interest in sharing those reports with patients, necessitating providing them patient-friendly simplifications of the original reports. This study explores the suitability of large language models in automatically generating those simplifications. We examine the usefulness of chain-of-thought and self-correction prompting mechanisms in this domain. We also propose a new evaluation protocol that employs radiologists and laypeople, where radiologists verify the factual correctness of simplifications, and laypeople assess simplicity and comprehension. Our experimental results demonstrate the effectiveness of self-correction prompting in producing high-quality simplifications. Our findings illuminate the preferences of radiologists and laypeople regarding text simplification, informing future research on this topic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_18859 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification Yang, Ziyu Cherian, Santhosh Vucetic, Slobodan Computation and Language Artificial Intelligence Radiology reports are highly technical documents aimed primarily at doctor-doctor communication. There has been an increasing interest in sharing those reports with patients, necessitating providing them patient-friendly simplifications of the original reports. This study explores the suitability of large language models in automatically generating those simplifications. We examine the usefulness of chain-of-thought and self-correction prompting mechanisms in this domain. We also propose a new evaluation protocol that employs radiologists and laypeople, where radiologists verify the factual correctness of simplifications, and laypeople assess simplicity and comprehension. Our experimental results demonstrate the effectiveness of self-correction prompting in producing high-quality simplifications. Our findings illuminate the preferences of radiologists and laypeople regarding text simplification, informing future research on this topic. |
| title | Two-Pronged Human Evaluation of ChatGPT Self-Correction in Radiology Report Simplification |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2406.18859 |