EMSDialog is a dataset of 4,414 synthetic multi-speaker EMS dialogues generated by a multi-LLM agent pipeline grounded in ePCR reports, annotated with diagnoses, roles, and topics, and shown to improve accuracy, timeliness, and stability in conversational diagnosis prediction.
InProceedings of the 44th International ACM SIGIR Conference on Re- search and Development in Information Retrieval, pages 544–554
2 Pith papers cite this work. Polarity classification is still indexing.
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FACT-E uses controlled perturbations as an instrumental signal to measure intra-chain faithfulness in CoT reasoning and combines it with answer consistency to select trustworthy trajectories.
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EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents
EMSDialog is a dataset of 4,414 synthetic multi-speaker EMS dialogues generated by a multi-LLM agent pipeline grounded in ePCR reports, annotated with diagnoses, roles, and topics, and shown to improve accuracy, timeliness, and stability in conversational diagnosis prediction.
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FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning
FACT-E uses controlled perturbations as an instrumental signal to measure intra-chain faithfulness in CoT reasoning and combines it with answer consistency to select trustworthy trajectories.