Hallucination Detection-Guided Preference Optimization for Clinical Summarization
📰 ArXiv cs.AI
Learn to optimize clinical summarization using hallucination detection-guided preference optimization with LLMs
Action Steps
- Implement a hallucination detector to identify unsupported statements in clinical summaries
- Use the hallucination detector to guide iterative summary revisions toward factual corrections
- Evaluate the performance of the revised summaries using metrics such as factual accuracy and clinical relevance
- Fine-tune the LLM using the revised summaries to improve its overall performance
- Test the optimized model on a held-out dataset to assess its reliability and effectiveness
Who Needs to Know This
Data scientists and NLP engineers working on clinical summarization tasks can benefit from this approach to improve the reliability of their models
Key Insight
💡 Hallucination detection can be used to guide iterative summary revisions and improve the factual accuracy of clinical summaries
Share This
Optimize clinical summarization with hallucination detection-guided preference optimization #LLMs #ClinicalSummarization
Key Takeaways
Learn to optimize clinical summarization using hallucination detection-guided preference optimization with LLMs
Full Article
Title: Hallucination Detection-Guided Preference Optimization for Clinical Summarization
Abstract:
arXiv:2605.28910v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce \itermodelfull (\itermodel), an inference-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections. Building on this, we propose \itermodel for Preferenc
Abstract:
arXiv:2605.28910v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise on summarization tasks, but they often produce hallucinations, which are unsupported or incorrect statements that limit their reliability in specialized healthcare applications. We introduce \itermodelfull (\itermodel), an inference-time method that leverages hallucination detectors to guide iterative summary revisions toward factual corrections. Building on this, we propose \itermodel for Preferenc
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