---
title: "Linking Knowledge to Care — paper by Sun et al. (2026)"
description: "The authors propose KG-Followup, which connects LLM question generation to a biomedical knowledge graph. 26 references."
authors: ["Liwen Sun", "Xiang Yu", "Ming Tan", "Zhuohao Chen", "Anqi Cheng", "Ashutosh Joshi", "Chenyan Xiong"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.43/"
doi: "10.18653/v1/2026.findings-eacl.43"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sun-2026-linking-knowledge-care-knowledge-graph/"
---
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# Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation

**Authors:** Liwen Sun, [Xiang Yu](https://md.research.elamur.ai/free/en/authors/xiang-yu/), Ming Tan, Zhuohao Chen, Anqi Cheng, Ashutosh Joshi and [Chenyan Xiong](https://md.research.elamur.ai/free/en/authors/chenyan-xiong/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.43/) · [PDF](https://aclanthology.org/2026.findings-eacl.43.pdf) · [DOI 10.18653/v1/2026.findings-eacl.43](https://doi.org/10.18653/v1/2026.findings-eacl.43) · [OpenAlex](https://openalex.org/W7140108832)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Before a diagnosis, clinicians spend much of their time collecting patient history, and gaps in that history are a known source of diagnostic error. LLMs could help by asking follow-up questions, but they often overlook symptoms because their medical knowledge is limited. The authors propose KG-Followup, which connects LLM question generation to a biomedical knowledge graph. The pipeline first lets the LLM ask its own clarification questions. It then extracts clinical entities as a stand-in for an EHR and adds questions from three KG-based steps: retrieval of shared neighbouring concepts, reasoning paths from symptoms to differential diagnoses (DDX), and in-context examples drawn from cases the graph cannot map ("hard" cases). Finally, overlapping questions are clustered and merged. The authors also build ClinicalInquiryBench, a benchmark with importance weights derived from HealthBench conversations. On it and on FollowupBench, KG-Followup reaches about 70–80% weighted recall and beats the multi-agent FollowupQ baseline by roughly 5–8%. It matches the coverage of much longer zero-shot question lists with fewer questions, which matters for keeping patient interactions short.

## Key points

- **ClinicalInquiryBench:** 5,000 HealthBench cases were filtered to 1,498 context-seeking instances with weighted reference questions (about 5 per case), split into 250 dev and 1,248 test. FollowupBench has 250 expert-curated cases with about 9 unweighted questions each.
- **Evaluation:** weighted recall judged by an LLM. Backbones are Claude Haiku and Claude Sonnet, with GPT-4o and MedGemma-27b tested only as Zero-Shot-k baselines.
- **Best results (Claude Sonnet with KG-informed active ICL):** 0.75/0.77 recall on ClinicalInquiryBench dev/test with 20 questions, versus 0.65/0.66 for FollowupQ. On FollowupBench it reaches 0.82 with 40 questions, versus 0.74.
- **Ablations:**
  - The DDX-guided module contributes more than the EHR-guided one, and the choice among KG retrieval variants matters little.
  - Clustering-based consolidation cuts the question pool from 51 to 40 with only a small loss (0.79 → 0.77). Having an LLM select the questions drops recall to 0.71.
  - Unsupervised selection of KG-based hard examples performs on par with supervised selection of hard cases.
- **Limitations:** the method depends on a comprehensive, current knowledge graph, and the evaluation is offline only.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Liwen Sun, Xiang Yu, Ming Tan, Zhuohao Chen, Anqi Cheng, Ashutosh Joshi and Chenyan Xiong. 2026. Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{sun2026linking,
  title = {{Linking Knowledge to Care: Knowledge Graph-Augmented Medical Follow-Up Question Generation}},
  author = {Liwen Sun, Xiang Yu, Ming Tan, Zhuohao Chen, Anqi Cheng, Ashutosh Joshi and Chenyan Xiong},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.43},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.43}
}
```

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*Updated 1 November 2026*