---
title: "Faithful Medical Dialogue Generation Using — Priya 2026"
description: "LLMs used for medical dialogue generation (MDG) often hallucinate."
authors: ["Priyanshu Priya", "Hardik Goyal", "Asif Ekbal"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.4/"
doi: "10.63317/43dzmqsdheef"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/priya-2026-faithful-medical-dialogue-generation-homo/"
---
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# Faithful Medical Dialogue Generation Using Homo-Heterogeneous Exemplar-based In-Context Knowledge Grounding

**Authors:** Priyanshu Priya, Hardik Goyal, [Asif Ekbal](https://md.research.elamur.ai/free/en/authors/asif-ekbal/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.4/) · [PDF](https://aclanthology.org/2026.lrec-1.4.pdf) · [DOI 10.63317/43dzmqsdheef](https://doi.org/10.63317/43dzmqsdheef) · [OpenAlex](https://openalex.org/W7160087977)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
LLMs used for medical dialogue generation (MDG) often hallucinate. They can contradict even correct knowledge they are given, which is dangerous in clinical advice. The authors present IMKDG (In-context Medical Knowledge-grounded Dialogue Generator), a plug-and-play retrieval framework that needs no extra knowledge-encoding modules. It combines external medical knowledge with in-context learning (ICL) and has three parts:

- **Medical Knowledge Enrichment (MKE):** finds medical concepts in the patient–doctor history with QuickUMLS and builds a small knowledge graph of triplets using relations from the UMLS Semantic Network.
- **Homo-Heterogeneous Exemplar Selection (H2ES):** picks demonstration examples that are both relevant and varied, judged by dialogue context and attached knowledge.
- **Prompt Construction (PC):** assembles the final prompt.

The framework was tested with seven LLMs on the MedDialog-EN and CDialog datasets. Automatic and human evaluations indicate fewer hallucinations and more faithful responses. The method is model-agnostic and does not require fine-tuning, so it can be applied to other LLMs and medical corpora.

## Key points
- **Task setup:** MDG is cast as ICL. The model generates the next doctor turn from the alternating patient/doctor history plus retrieved knowledge K.
- **Knowledge graph construction:** each UMLS CUI becomes a node, and edges come from the UMLS Semantic Network. An example triplet is (coronavirus, affects, respiratory system).
- **Models evaluated:** BioGPT, ChatGPT, MedAlpaca, ChatDoctor, PMC-LLaMA, MedPaLM 2 and GPT-4.
- **Datasets:** MedDialog-EN and CDialog (a multi-turn COVID-19 conversation corpus).
- **Code and resources:** released at github.com/priyanshu-profile/LREC26_IMKDG. The excerpt provided did not include the numeric result tables, so specific scores are not reported here.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

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

```bibtex
@inproceedings{priya2026faithful,
  title = {{Faithful Medical Dialogue Generation Using Homo-Heterogeneous Exemplar-based In-Context Knowledge Grounding}},
  author = {Priya, Priyanshu and Goyal, Hardik and Ekbal, Asif},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/43dzmqsdheef},
  url = {https://doi.org/10.63317/43dzmqsdheef}
}
```

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