SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) struggle with factual inaccuracies, a critical issue in clinical NLP applications where errors could lead to serious consequences. |
| Approach: | They propose a pipeline that leverages >100B parameter GPT variants to act as synthetic experts to generate edit feedback without additional human annotations. |
| Outcome: | The proposed pipeline aims to improve the quality of clinical note summarizations by generating edit feedback without human annotations. |
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