Summary of Synfac-edit: Synthetic Imitation Edit Feedback For Factual Alignment in Clinical Summarization, by Prakamya Mishra et al.
SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization
by Prakamya Mishra, Zonghai Yao, Parth Vashisht, Feiyun Ouyang, Beining Wang, Vidhi Dhaval Mody, Hong Yu
First submitted to arxiv on: 21 Feb 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 study tackles the challenge of factual inaccuracies in Large Language Models (LLMs) like GPT and Llama, which are crucial in clinical NLP applications. To address this issue without relying on expert-annotated data, the researchers introduce a novel pipeline that utilizes GPT variants (>100B parameters) as synthetic experts to generate high-quality feedback for enhancing factual consistency in clinical note summarization. The study focuses on edit feedback generated by these synthetic experts without additional human annotations, mirroring real-world scenarios where medical professionals refine AI system outputs. By leveraging 100B+ GPT variants as synthetic feedback experts, the authors aim to narrow the gap between AI-generated content and factual accuracy using two distinct alignment algorithms (DPO & SALT). The potential of LLM-based synthetic edits in enhancing clinical factuality is substantial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make computer programs that summarize text more accurate. Right now, these programs can sometimes get important facts wrong, which could have serious consequences. To fix this problem without needing a lot of expert help, the researchers use really smart computer models (GPT) to act like experts and give feedback on how to make the summaries better. They test this approach by using it to improve the accuracy of smaller computer models that summarize medical texts. This research can help make AI-generated content more trustworthy. |
Keywords
» Artificial intelligence » Alignment » Gpt » Llama » Nlp » Summarization
