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A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models

23 Feb 2024arXiv:2402.15422archive 2025-07-28

Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse, Monica Agrawal, David Sontag, Xiaoyi Jiang

Patients often face difficulties in understanding their hospitalizations, while healthcare workers have limited resources to provide explanations. In this work, we investigate the potential of large language models to generate patient summaries based on doctors' notes and study the effect of training data on the faithfulness and quality of the generated summaries. To this end, we release (i) a rigorous labeling protocol for errors in medical texts and (ii) a publicly available dataset of annotated hallucinations in 100 doctor-written and 100 generated summaries. We show that fine-tuning on hallucination-free data effectively reduces hallucinations from 2.60 to 1.55 per summary for Llama 2, while preserving relevant information. We observe a similar effect on GPT-4 (0.70 to 0.40), when the few-shot examples are hallucination-free. We also conduct a qualitative evaluation using hallucination-free and improved training data. We find that common quantitative metrics do not correlate well with faithfulness and quality. Finally, we test GPT-4 for automatic hallucination detection, which clearly outperforms common baselines.

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create_icl_example_v1 stefanhgm/patient_summaries_with_llms/gpt-4/run_hallucination_detection.py official repository ran · our draft was wrong MIT (permissive) · 1dfa22c583d35631 · report
extract_di stefanhgm/patient_summaries_with_llms/preprocess/process_mimic_summaries.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 34f40108fd90e5f1 · report
extract_hc stefanhgm/patient_summaries_with_llms/preprocess/process_mimic_summaries.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · d6b6f331b351e1a3 · report
parse_hallucination_detection_results stefanhgm/patient_summaries_with_llms/gpt-4/run_hallucination_detection.py official repository ran · our draft was wrong MIT (permissive) · c40d85bacef9a266 · report
remove_empty_and_short_summaries stefanhgm/patient_summaries_with_llms/preprocess/process_mimic_summaries.py official repository ran · our draft was wrong MIT (permissive) · 115edd746212499f · report

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Hallucination

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutGPT-4Label SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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