@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation
%A Sun, Liwen
%A Yu, Xiang
%A Tan, Ming
%A Chen, Zhuohao
%A Cheng, Anqi
%A Joshi, Ashutosh
%A Xiong, Chenyan
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F sun-etal-2026-linking
%X 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.
%U https://aclanthology.org/2026.findings-eacl.43/
%P 846-853
Markdown (Informal)
[Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation](https://aclanthology.org/2026.findings-eacl.43/) (Sun et al., Findings 2026)
ACL