---
title: "Iterative Knowledge Graph Refinement and Integration for"
description: "Zou and Wang propose an iterative medical QA framework that builds a small but sufficient evidence subgraph instead. 33 references."
authors: ["Haochen Zou", "Yongli Wang"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.408/"
doi: "10.18653/v1/2026.findings-acl.408"
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/zou-2026-iterative-knowledge-graph-refinement-integration/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zou-2026-iterative-knowledge-graph-refinement-integration/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/37/) / Iterative Knowledge Graph Refinement and Integration for Medical Question Answering

# Iterative Knowledge Graph Refinement and Integration for Medical Question Answering

**Authors:** Haochen Zou and Yongli Wang · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 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-acl.408/) · [PDF](https://aclanthology.org/2026.findings-acl.408.pdf) · [DOI 10.18653/v1/2026.findings-acl.408](https://doi.org/10.18653/v1/2026.findings-acl.408) · [OpenAlex](https://openalex.org/W7166843117)

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

## Summary
LLMs often hallucinate on specialized medical questions. Graph-based RAG adds knowledge graph (KG) facts, but it tends to retrieve redundant or noisy subgraphs that confuse the model. Zou and Wang propose an iterative medical QA framework that builds a small but sufficient evidence subgraph instead. The framework works in four steps:

- A domain-specific dense retriever selects top-ranked entities and relations for the question.
- The system expands through KG neighbors hop by hop, choosing them by relevance score.
- It prunes triplets using graph neural network representations of the subgraph.
- It repeats these steps until a sufficiency classifier judges the evidence adequate.

The final subgraph is encoded as a multi-level list that keeps both breadth (neighbors) and depth (traversal paths), and the LLM uses it to predict the answer. On three multiple-choice medical QA benchmarks, the authors report state-of-the-art results against RAG and graph-based RAG baselines. Ablations show clear drops when relevance-based selection or structure-based pruning is removed. The study argues that limiting redundancy during both retrieval and refinement is central to reliable KG-augmented medical QA.

## Key points
- The pipeline has four modules: Factual Information Retrieval, Evidence Subgraph Construction, Evidence Subgraph Pruning, and Answer Prediction. It loops until the subgraph is classified as sufficient.
- Neighbor exploration is limited to 2-hop neighbors, which the authors list as a limitation for KGs that need deeper relational paths.
- Compared baselines include RAG methods (MedRAG, RGAR, RAG 2), graph-based RAG methods (GraphRAG, ByoKG-RAG, AMG-RAG), and medical LLMs (Meditron, BioMistral, Meerkat-7B).
- Other stated limitations:
  - The framework uses only open-source LLMs and does not support API-based closed models.
  - Evaluation covers multiple-choice questions only, not open-ended ones.
  - Retrieval relies on a bag-of-words representation of the question.
  - Its scope is restricted to medicine, with no design for other domains.
- The excerpt provided does not include benchmark names or accuracy figures, so check the full paper before citing specific numbers.

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

## Institutions

- [Nanjing University of Science and Technology](https://md.research.elamur.ai/free/en/universities/nanjing-university-of-science-and-technology/) — University, Nanjing, China · 19 works · 1,590 citations

📚 Shelves (7): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🧬 Biochemistry, Genetics and Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · 263 works · [🧬 Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/) · 260 works

🧭 Topics (2): [❓ Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · 487 works · [🕸️ Knowledge graphs & ontologies](https://md.research.elamur.ai/free/en/topics/knowledge-graphs/) · 211 works

## Cite

Cite as: Haochen Zou and Yongli Wang. 2026. Iterative Knowledge Graph Refinement and Integration for Medical Question Answering. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{zou2026iterative,
  title = {{Iterative Knowledge Graph Refinement and Integration for Medical Question Answering}},
  author = {Haochen Zou and Yongli Wang},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.408},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.408}
}
```

- [← previous Iterative Dual-Model Alignment for Story Evaluation](https://md.research.elamur.ai/free/en/papers/qin-2026-iterative-dual-model-alignment-story/)
- [next → Iterative Self-Correction for Text-Driven Person Re-Identification with Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/luo-2026-iterative-self-correction-text-driven/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/37/)

## References

33 references, 13 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, and Shyamal Anadkat. 2023. [GPT-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774.
2. Zeming Chen, Alejandro Hernández Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne H… — [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2023-meditron-70b-scaling-medical-pretraining/)
3. Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. 2024. From local to global: A graph RAG approach to query-focused summarization. arXiv preprint arXiv:2404.16130. [find](https://scholar.google.com/scholar?q=Darren+Edge%2C+Ha+Trinh%2C+Newman+Cheng%2C+Joshua+Bradley%2C+Alex+Chao%2C+Apurva+Mody%2C+Steven+Truitt%2C+Dasha+Metropolitansky%2C+Robert+Osazuwa+Ness%2C+and+Jonathan+Larson%2E+2024%2E+From+local+to+global%3A+A+graph+RAG+app)
4. Giacomo Frisoni, Alessio Cocchieri, Alex Presepi, Gianluca Moro, and Zaiqiao Meng. 2024. To generate or to retrieve? On the effectiveness of artificial contexts for medical open-domain question answering. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Vol… [find](https://scholar.google.com/scholar?q=Giacomo+Frisoni%2C+Alessio+Cocchieri%2C+Alex+Presepi%2C+Gianluca+Moro%2C+and+Zaiqiao+Meng%2E+2024%2E+To+generate+or+to+retrieve%3F+On+the+effectiveness+of+artificial+contexts+for+medical+open%2Ddomain+question+answer)
5. Guangze Gao, Zixuan Li, Chunfeng Yuan, Jiawei Li, Wu Jianzhuo, Yuehao Zhang, Xiaolong Jin, Bing Li, and Weiming Hu. 2025. D-RAG: Differentiable retrieval-augmented generation for knowledge graph question answering. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Proces… [find](https://scholar.google.com/scholar?q=Guangze+Gao%2C+Zixuan+Li%2C+Chunfeng+Yuan%2C+Jiawei+Li%2C+Wu+Jianzhuo%2C+Yuehao+Zhang%2C+Xiaolong+Jin%2C+Bing+Li%2C+and+Weiming+Hu%2E+2025%2E+D%2DRAG%3A+Differentiable+retrieval%2Daugmented+generation+for+knowledge+graph+quest)
6. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, and Alex Vaughan. 2024. [The LLaMA 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
7. Bolei He, Xinran He, Run Shao, Shanfu Shu, Xianwei Xue, MingQuan Cheng, Haifeng Li, and Zhen-Hua Ling. 2025. Select to know: An internal-external knowledge self-selection framework for domain-specific question answering. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages… [find](https://scholar.google.com/scholar?q=Bolei+He%2C+Xinran+He%2C+Run+Shao%2C+Shanfu+Shu%2C+Xianwei+Xue%2C+MingQuan+Cheng%2C+Haifeng+Li%2C+and+Zhen%2DHua+Ling%2E+2025%2E+Select+to+know%3A+An+internal%2Dexternal+knowledge+self%2Dselection+framework+for+domain%2Dspecific)
8. RJE: A retrieval-judgment-exploration framework for efficient knowledge graph question answering with LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 17288–17305. [find](https://scholar.google.com/scholar?q=RJE%3A+A+retrieval%2Djudgment%2Dexploration+framework+for+efficient+knowledge+graph+question+answering+with+LLMs%2E+In+Proceedings+of+the+2025+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+p)
9. Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, and Jie Jiang. 2024. Understanding the ranking loss for recommendation with sparse user feedback. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 5409–5418. [find](https://scholar.google.com/scholar?q=Zhutian+Lin%2C+Junwei+Pan%2C+Shangyu+Zhang%2C+Ximei+Wang%2C+Xi+Xiao%2C+Shudong+Huang%2C+Lei+Xiao%2C+and+Jie+Jiang%2E+2024%2E+Understanding+the+ranking+loss+for+recommendation+with+sparse+user+feedback%2E+In+Proceedings+o)
10. Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis, Zhen Han, Qi Zhu, Ian Robinson, Bryan Thompson, Huzefa Rangwala, and George Karypis. 2025. BYOKG-RAG: Multi-strategy graph retrieval for knowledge graph question answering. In Proceedings of the 2025 Conference on Empirical Methods in Natural… [find](https://scholar.google.com/scholar?q=Costas+Mavromatis%2C+Soji+Adeshina%2C+Vassilis+N%2E+Ioannidis%2C+Zhen+Han%2C+Qi+Zhu%2C+Ian+Robinson%2C+Bryan+Thompson%2C+Huzefa+Rangwala%2C+and+George+Karypis%2E+2025%2E+BYOKG%2DRAG%3A+Multi%2Dstrategy+graph+retrieval+for+knowle)
11. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. [Measuring massive multitask language understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/). In International Conference on Learning Representations.
12. Costas Mavromatis and George Karypis. 2022. ReaRev: Adaptive reasoning for question answering over knowledge graphs. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2447–2458. [find](https://scholar.google.com/scholar?q=Costas+Mavromatis+and+George+Karypis%2E+2022%2E+ReaRev%3A+Adaptive+reasoning+for+question+answering+over+knowledge+graphs%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2022%2C+pages+244)
13. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,… [find](https://scholar.google.com/scholar?q=Albert+Q%2E+Jiang%2C+Alexandre+Sablayrolles%2C+Arthur+Mensch%2C+Chris+Bamford%2C+Devendra+Singh+Chaplot%2C+Diego+de+las+Casas%2C+Florian+Bressand%2C+Gianna+Lengyel%2C+Guillaume+Lample%2C+Lucile+Saulnier%2C+L%C3%A9lio+Renard+Lav)
14. Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022. [MedMCQA: A large-scale multi-subject multi-choice dataset for medical domain question answering](https://md.research.elamur.ai/free/en/papers/pal-2022-medmcqa-large-scale-multi-subject/). In Proceedings of the Conference on Health, Inference, and Learning, pages 248–260.
15. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021. [What disease does this patient have? A large-scale open domain question answering dataset from medical exams](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/). Applied Sciences, 11(14):6421.
16. Jianing Qiu, Kyle Lam, Guohao Li, Amish Acharya, Tien Yin Wong, Ara Darzi, Wu Yuan, and Eric J Topol. 2024. LLM-based agentic systems in medicine and healthcare. Nature Machine Intelligence, 6(12):1418–1420. [find](https://scholar.google.com/scholar?q=Jianing+Qiu%2C+Kyle+Lam%2C+Guohao+Li%2C+Amish+Acharya%2C+Tien+Yin+Wong%2C+Ara+Darzi%2C+Wu+Yuan%2C+and+Eric+J+Topol%2E+2024%2E+LLM%2Dbased+agentic+systems+in+medicine+and+healthcare%2E+Nature+Machine+Intelligence%2C+6%2812%29%3A141)
17. Hyunjae Kim, Hyeon Hwang, Jiwoo Lee, Sihyeon Park, Dain Kim, Taewhoo Lee, Chanwoong Yoon, Jiwoong Sohn, Donghee Choi, and Jaewoo Kang. 2024. [Small language models learn enhanced reasoning skills from medical textbooks](https://md.research.elamur.ai/free/en/papers/kim-2024-small-language-models-learn-enhanced/). arXiv preprint arXiv:2404.00376.
18. Mohammad Reza Rezaei, Reza Saadati Fard, Jayson Lee Parker, Rahul G Krishnan, and Milad Lankarany. 2025. Agentic medical knowledge graphs enhance medical question answering: Bridging the gap between LLMs and evolving medical knowledge. In Findings of the Association for Computational Linguistics: EM… [find](https://scholar.google.com/scholar?q=Mohammad+Reza+Rezaei%2C+Reza+Saadati+Fard%2C+Jayson+Lee+Parker%2C+Rahul+G+Krishnan%2C+and+Milad+Lankarany%2E+2025%2E+Agentic+medical+knowledge+graphs+enhance+medical+question+answering%3A+Bridging+the+gap+between+L)
19. Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour. 2024. [BioMistral: A collection of open-source pretrained large language models for medical domains](https://md.research.elamur.ai/free/en/papers/labrak-2024-biomistral-collection-open-source-pretrained/). In Findings of the Association for Computational Linguistics: ACL 2024, pages 5848–5864.
20. Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, and Stephen Pfohl. 2023. [Large language models encode clinical knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/). Nature, 620(7972):172–180.
21. Sichu Liang, Linhai Zhang, Hongyu Zhu, Wenwen Wang, Yulan He, and Deyu Zhou. 2025. RGAR: Recurrence generation-augmented retrieval for factual-aware medical question answering. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4006–4033. [find](https://scholar.google.com/scholar?q=Sichu+Liang%2C+Linhai+Zhang%2C+Hongyu+Zhu%2C+Wenwen+Wang%2C+Yulan+He%2C+and+Deyu+Zhou%2E+2025%2E+RGAR%3A+Recurrence+generation%2Daugmented+retrieval+for+factual%2Daware+medical+question+answering%2E+In+Findings+of+the+Asso)
22. Can Lin, Zhengwang Jiang, Ling Zheng, Qi Zhao, Yuhang Zhang, Qi Song, and Wangqiu Zhou. 2025. [find](https://scholar.google.com/scholar?q=Can+Lin%2C+Zhengwang+Jiang%2C+Ling+Zheng%2C+Qi+Zhao%2C+Yuhang+Zhang%2C+Qi+Song%2C+and+Wangqiu+Zhou%2E+2025%2E)
23. Jiwoong Sohn, Yein Park, Chanwoong Yoon, Sihyeon Park, Hyeon Hwang, Mujeen Sung, Hyunjae Kim, and Jaewoo Kang. 2025. Rationale-guided retrieval augmented generation for medical question answering. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Com… [find](https://scholar.google.com/scholar?q=Jiwoong+Sohn%2C+Yein+Park%2C+Chanwoong+Yoon%2C+Sihyeon+Park%2C+Hyeon+Hwang%2C+Mujeen+Sung%2C+Hyunjae+Kim%2C+and+Jaewoo+Kang%2E+2025%2E+Rationale%2Dguided+retrieval+augmented+generation+for+medical+question+answering%2E+In+)
24. Yuan Sui, Yufei He, Zifeng Ding, and Bryan Hooi. 2025a. [Can knowledge graphs make large language models more trustworthy? An empirical study over open-ended question answering](https://md.research.elamur.ai/free/en/papers/sui-2025-can-knowledge-graphs-make-large/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1…
25. Algorithm 1: Medical Question Answering Input: Question Q; Knowledge Graph G; Entity Set E; Relation Set R. Output: Final Answer Â. Initialization: G sub ← ∅; E sub ← ∅; R sub ← ∅. (i) Factual Information Retrieval: E ret ← top−k(Sim(Q, E)); R ret ← top−k(Sim(Q, R)); for i ← 1 to k do (ii) Evidence… [find](https://scholar.google.com/scholar?q=Algorithm+1%3A+Medical+Question+Answering+Input%3A+Question+Q%3B+Knowledge+Graph+G%3B+Entity+Set+E%3B+Relation+Set+R%2E+Output%3A+Final+Answer+A%CC%82%2E+Initialization%3A+G+sub+%E2%86%90+%E2%88%85%3B+E+sub+%E2%86%90+%E2%88%85%3B+R+sub+%E2%86%90+%E2%88%85%2E+%28i%29+Factual+Inform)
26. Junhong Wan, Tao Yu, Kunyu Jiang, Yao Fu, Weihao Jiang, and Jiang Zhu. 2025. Digest the knowledge: Large language models empowered message passing for knowledge graph question answering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers… [find](https://scholar.google.com/scholar?q=Junhong+Wan%2C+Tao+Yu%2C+Kunyu+Jiang%2C+Yao+Fu%2C+Weihao+Jiang%2C+and+Jiang+Zhu%2E+2025%2E+Digest+the+knowledge%3A+Large+language+models+empowered+message+passing+for+knowledge+graph+question+answering%2E+In+Proceeding)
27. Fengyi Wang, Guanghui Zhu, Chunfeng Yuan, and Yihua Huang. 2024. LLM-enhanced cascaded multi-level learning on temporal heterogeneous graphs. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 512–521. [find](https://scholar.google.com/scholar?q=Fengyi+Wang%2C+Guanghui+Zhu%2C+Chunfeng+Yuan%2C+and+Yihua+Huang%2E+2024%2E+LLM%2Denhanced+cascaded+multi%2Dlevel+learning+on+temporal+heterogeneous+graphs%2E+In+Proceedings+of+the+47th+International+ACM+SIGIR+Confere)
28. Shuai Wang and Yinan Yu. 2025. iQUEST: An iterative question-guided framework for knowledge base question answering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15616– 15628. [find](https://scholar.google.com/scholar?q=Shuai+Wang+and+Yinan+Yu%2E+2025%2E+iQUEST%3A+An+iterative+question%2Dguided+framework+for+knowledge+base+question+answering%2E+In+Proceedings+of+the+63rd+Annual+Meeting+of+the+Association+for+Computational+Ling)
29. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in Neural Information Processing Systems, 35:24824–24837.
30. Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang. 2024. [Benchmarking retrieval-augmented generation for medicine](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/). In Findings of the Association for Computational Linguistics: ACL 2024, pages 6233–6251.
31. Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce C. Ho, Chao Zhang, and Carl Yang. 2024. BMRetriever: Tuning large language models as better biomedical text retrievers. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 2223… [find](https://scholar.google.com/scholar?q=Ran+Xu%2C+Wenqi+Shi%2C+Yue+Yu%2C+Yuchen+Zhuang%2C+Yanqiao+Zhu%2C+May+Dongmei+Wang%2C+Joyce+C%2E+Ho%2C+Chao+Zhang%2C+and+Carl+Yang%2E+2024%2E+BMRetriever%3A+Tuning+large+language+models+as+better+biomedical+text+retrievers%2E+I)
32. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, and Chenxu Lv. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). arXiv preprint arXiv:2505.09388.
33. Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou, and Jian-Yun Nie. 2024. A multi-task embedder for retrieval augmented LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3537–3553. [find](https://scholar.google.com/scholar?q=Peitian+Zhang%2C+Zheng+Liu%2C+Shitao+Xiao%2C+Zhicheng+Dou%2C+and+Jian%2DYun+Nie%2E+2024%2E+A+multi%2Dtask+embedder+for+retrieval+augmented+LLMs%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Association+for+Comput)

*Updated 2 November 2026*