@inproceedings{sun-etal-2026-linking,
    title = "Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation",
    author = "Sun, Liwen  and
      Yu, Xiang  and
      Tan, Ming  and
      Chen, Zhuohao  and
      Cheng, Anqi  and
      Joshi, Ashutosh  and
      Xiong, Chenyan",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.43/",
    pages = "846--853",
    ISBN = "979-8-89176-386-9",
    abstract = "Clinical diagnosis is time-consuming, requiring intensive interactions between patients and medical professionals. While large language models (LLMs) could ease the pre-diagnostic workload, their limited domain knowledge hinders effective medical question generation. We introduce a Knowledge Graph-augmented LLM with active in-context learning to generate relevant and important follow-up questions, KG-Followup, serving as a critical module for the pre-diagnostic assessment. The structured medical domain knowledge graph serves as a seamless patch-up to provide professional domain expertise upon which the LLM can reason. Experiments demonstrate that KG-Followup outperforms state-of-the-art methods by 5{\%} - 8{\%} on relevant benchmarks."
}