UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?
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Abstract (arXiv)
This paper presents UMASS_BioNLP team participation in the MEDIQA-Chat 2023 shared task for Task-A and Task-C. We focus especially on Task-C and propose a novel LLMs cooperation system named a doctor-patient loop to generate high-quality conversation data sets. The experiment results demonstrate that our approaches yield reasonable performance as evaluated by automatic metrics such as ROUGE, medical concept recall, BLEU, and Self-BLEU. Furthermore, we conducted a comparative analysis between our proposed method and ChatGPT and GPT-4. This analysis also investigates the potential of utilizing cooperation LLMs to generate high-quality datasets.
Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 49 of its 80 references that are in the map. First posted on arXiv 29 Jun 2023.
Builds on
49 of 80 references.
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Citing papers per year: 2023: 6; 2024: 3; 2025: 3; 2026: 2.
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