@inproceedings{jia-etal-2025-ddo,
    title = "{DDO}: Dual-Decision Optimization for {LLM}-Based Medical Consultation via Multi-Agent Collaboration",
    author = "Jia, Zhihao  and
      Jia, Mingyi  and
      Duan, Junwen  and
      Wang, Jianxin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1340/",
    pages = "26380--26397",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Language Models (LLMs) demonstrate strong generalization and reasoning abilities, making them well-suited for complex decision-making tasks such as medical consultation (MC). However, existing LLM-based methods often fail to capture the dual nature of MC, which entails two distinct sub-tasks: symptom inquiry, a sequential decision-making process, and disease diagnosis, a classification problem. This mismatch often results in ineffective symptom inquiry and unreliable disease diagnosis. To address this, we propose $\textbf{DDO}$, a novel LLM-based framework that performs $\textbf{D}$ual-$\textbf{D}$ecision $\textbf{O}$ptimization by decoupling the two sub-tasks and optimizing them with distinct objectives through a collaborative multi-agent workflow. Experiments on three real-world MC datasets show that DDO consistently outperforms existing LLM-based approaches and achieves competitive performance with state-of-the-art generation-based methods, demonstrating its effectiveness in the MC task. The code is available at https://github.com/zh-jia/DDO."
}