---
title: "MHGraphBench — paper by Liu et al. (2026)"
description: "The authors build MHGraphBench from PrimeKG. 40 references. Among them: 0."
authors: ["Weixin Liu", "Congning Ni", "Shelagh A. Mulvaney", "Susannah L. Rose", "Murat Kantarcioglu", "Bradley A. Malin", "Zhijun Yin"]
year: 2026
venue: "Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)"
source: "https://aclanthology.org/2026.gem-main.38/"
doi: "10.18653/v1/2026.gem-main.38"
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/liu-2026-mhgraphbench-knowledge-graph-grounded-benchmarking/"
---
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# MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models

**Authors:** [Weixin Liu](https://md.research.elamur.ai/free/en/authors/weixin-liu/), Congning Ni, Shelagh A. Mulvaney, Susannah L. Rose, Murat Kantarcioglu, [Bradley A. Malin](https://md.research.elamur.ai/free/en/authors/bradley-a-malin/) and Zhijun Yin · **Year:** 2026 · **Venue:** [Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/) ([GEM 2026](https://md.research.elamur.ai/free/en/venues/gem/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.gem-main.38/) · [PDF](https://aclanthology.org/2026.gem-main.38.pdf) · [DOI 10.18653/v1/2026.gem-main.38](https://doi.org/10.18653/v1/2026.gem-main.38) · [OpenAlex](https://openalex.org/W7166809780)

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

## Summary
Large language models are increasingly used in mental health, but broad medical benchmarks say little about whether they hold verifiable psychiatric biomedical knowledge. They also do not show whether models can separate safety-relevant relations, such as indication versus contraindication. The authors build MHGraphBench from PrimeKG. They start with 42 manually curated psychiatric disease nodes, take their 1-hop neighbourhood and keep clinically salient relation types. Graph facts are then automatically turned into multiple-choice items with controlled distractors. Nine task families cover entity typing, relation judgment and two-hop reasoning through a disease. Besides accuracy, the authors measure how much of the subgraph's entities, relations and triples each model handles correctly. They also test adding short KG-derived evidence snippets. Across 15 closed and open models, top systems nearly saturate entity typing but fall well behind on relation prediction and two-hop questions. Evidence snippets help some models and hurt others. Failures to follow the answer format noticeably distort scores. The benchmark is a reproducible, interpretable probe of structured mental-health knowledge. However, it measures agreement with a curated graph, not clinical safety.

## Key points
- The domain is defined by 42 psychiatric seed disease nodes in PrimeKG, whose diseases are based on MONDO. These were selected from 44 candidates; one syndrome was judged out of scope and "multiple personality disorder" was dropped as outdated terminology.
- There are nine multiple-choice task families with KG-backed gold answers and sampled negatives, plus coverage metrics over entities, relations and triples.
- Fifteen LLMs were evaluated. GPT-4.1 leads on average task accuracy, while GPT-5-mini reaches the highest triple coverage, so the two rankings diverge. Open-source models trail the strongest GPT-series systems.
- Contraindication relations and frequent entities such as anxiety disorder stay hard for all models.
- The labels inherit PrimeKG's gaps, and there was no expert validation of items. An edge missing from the subgraph does not mean the claim is false.

*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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- [Vanderbilt University Medical Center](https://md.research.elamur.ai/free/en/hospitals/vanderbilt-university-medical-center/) — Hospital, Nashville, United States · 3 works · 18 citations

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## Cite

Cite as: Weixin Liu, Congning Ni, Shelagh A. Mulvaney, Susannah L. Rose, Murat Kantarcioglu, Bradley A. Malin and Zhijun Yin. 2026. MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models. Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM).

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

```bibtex
@inproceedings{liu2026mhgraphbench,
  title = {{MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models}},
  author = {Weixin Liu, Congning Ni, Shelagh A. Mulvaney, Susannah L. Rose, Murat Kantarcioglu, Bradley A. Malin and Zhijun Yin},
  booktitle = {Proceedings of the Fifth Workshop on Generation, Evaluation and Metrics (GEM)},
  year = {2026},
  doi = {10.18653/v1/2026.gem-main.38},
  url = {https://doi.org/10.18653/v1/2026.gem-main.38}
}
```

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36. Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Mohamed Amin, Le Hou, Kevin Clark, Stephen R. Pfohl, Heather Cole-Lewis, Darlene Neal, Qazi Mamunur Rashid, Mike Schaekermann, Amy Wang, Dev Dash, Jonathan H. Chen, Nigam H. Shah, Sami Lachgar, Philip Andrew Mansfield, and 16 others… [find](https://scholar.google.com/scholar?q=Karan+Singhal%2C+Tao+Tu%2C+Juraj+Gottweis%2C+Rory+Sayres%2C+Ellery+Wulczyn%2C+Mohamed+Amin%2C+Le+Hou%2C+Kevin+Clark%2C+Stephen+R%2E+Pfohl%2C+Heather+Cole%2DLewis%2C+Darlene+Neal%2C+Qazi+Mamunur+Rashid%2C+Mike+Schaekermann%2C+Amy+W)
37. Hoyun Song, Migyeong Kang, Jisu Shin, Jihyun Kim, Chanbi Park, Hangyeol Yoo, Jihyun An, Alice Oh, Jinyoung Han, and KyungTae Lim. 2026. Mental-Bench: A benchmark for evaluating psychiatric diagnostic capability of large language models. arXiv preprint arXiv:2602.12871. — [arXiv:2602.12871](https://arxiv.org/abs/2602.12871) [find](https://scholar.google.com/scholar?q=Hoyun+Song%2C+Migyeong+Kang%2C+Jisu+Shin%2C+Jihyun+Kim%2C+Chanbi+Park%2C+Hangyeol+Yoo%2C+Jihyun+An%2C+Alice+Oh%2C+Jinyoung+Han%2C+and+KyungTae+Lim%2E+2026%2E+Mental%2DBench%3A+A+benchmark+for+evaluating+psychiatric+diagnostic+)
38. Zixin Xiong, Ziteng Wang, Haotian Fan, Xinjie Zhang, and Wenxuan Wang. 2026. TrustMH-Bench: A comprehensive benchmark for evaluating the trustworthiness of large language models in mental health. arXiv preprint arXiv:2603.03047. — [arXiv:2603.03047](https://arxiv.org/abs/2603.03047) [find](https://scholar.google.com/scholar?q=Zixin+Xiong%2C+Ziteng+Wang%2C+Haotian+Fan%2C+Xinjie+Zhang%2C+and+Wenxuan+Wang%2E+2026%2E+TrustMH%2DBench%3A+A+comprehensive+benchmark+for+evaluating+the+trustworthiness+of+large+language+models+in+mental+health%2E+arXi)
39. Chujie Zheng, Hao Zhou, Fandong Meng, Jie Zhou, and Minlie Huang. 2023. Large language models are not robust multiple choice selectors. arXiv preprint arXiv:2309.03882. [find](https://scholar.google.com/scholar?q=Chujie+Zheng%2C+Hao+Zhou%2C+Fandong+Meng%2C+Jie+Zhou%2C+and+Minlie+Huang%2E+2023%2E+Large+language+models+are+not+robust+multiple+choice+selectors%2E+arXiv+preprint+arXiv%3A2309%2E03882%2E)
40. Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, Qingqing Long, Yefeng Zheng, and Xian Wu. 2025. Can we trust AI doctors? a survey of medical hallucination in large language and large vision-language models. In Findings of the Association for Computational Linguistics:… [find](https://scholar.google.com/scholar?q=Zhihong+Zhu%2C+Yunyan+Zhang%2C+Xianwei+Zhuang%2C+Fan+Zhang%2C+Zhongwei+Wan%2C+Yuyan+Chen%2C+Qingqing+Long%2C+Yefeng+Zheng%2C+and+Xian+Wu%2E+2025%2E+Can+we+trust+AI+doctors%3F+a+survey+of+medical+hallucination+in+large+lang)

*Updated 1 November 2026*