Journal of the American Medical Informatics Association : JAMIA · 2026
CiteSure: retrieval-augmented large language models for faithful biomedical citation recommendation.
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Xie Qianqian, Zhang Jeffrey, Wang Yan, Huang Jimin, Lin Fongci, Weng Ruey-Ling, He Huan, Chen Qingyu, Xu Hua
Аннотация
OBJECTIVES: Accurate citation of relevant publications is essential for scientific integrity in biomedical research. Large language models (LLMs) excel at text generation but often hallucinate fabricated or inaccurate citations. Retrieval-augmented generation (RAG) can mitigate these errors, yet current approaches lack semantic precision in evidence retrieval. This study aims to develop a domain-specific RAG system for reliable, context-specific biomedical citation recommendations.
MATERIALS AND METHODS: We introduce CiteSure, a sentence-level citation recommendation tool designed to deliver reliable, evidence-based, and context-specific references using LLMs. CiteSure utilizes a 2-stage retrieval-augmented generation (RAG) framework, combining a domain-specific dense retriever (BioLLM2Vec) and reranker (BioRankLLaMA), adapted from LLaMA3-8B-Instruct using biomedical-specific training data. CiteSure leverages the complementary strengths of retrieval and generative LLM models, ensuring factual precision and contextual alignment. We evaluated CiteSure on a curated Alzheimer's disease dataset, comparing it to standalone LLMs and traditional retrieval-based methods.
RESULTS: CiteSure achieved 100% factual accuracy and the highest relevance score of 77.50%, outperforming all baselines. BioLLM2Vec retrieved relevant articles with over 80% accuracy in the top 100 candidates. BioRankLLaMA consistently outperformed baseline rerankers across MAP, MRR, and Precision@5 metrics, confirming the benefit of domain-specific adaptation and contrastive fine-tuning.
DISCUSSION AND CONCLUSION: Our results demonstrate that CiteSure, built on a 2-stage retrieval-augmented generation framework, effectively integrates domain-specific retrieval with LLM-based generation to achieve substantial improvements over baseline approaches. Our work underscores the importance of domain-specific adaptation in biomedical citation recommendation and provides publicly available datasets, models, and code for support future research.
MATERIALS AND METHODS: We introduce CiteSure, a sentence-level citation recommendation tool designed to deliver reliable, evidence-based, and context-specific references using LLMs. CiteSure utilizes a 2-stage retrieval-augmented generation (RAG) framework, combining a domain-specific dense retriever (BioLLM2Vec) and reranker (BioRankLLaMA), adapted from LLaMA3-8B-Instruct using biomedical-specific training data. CiteSure leverages the complementary strengths of retrieval and generative LLM models, ensuring factual precision and contextual alignment. We evaluated CiteSure on a curated Alzheimer's disease dataset, comparing it to standalone LLMs and traditional retrieval-based methods.
RESULTS: CiteSure achieved 100% factual accuracy and the highest relevance score of 77.50%, outperforming all baselines. BioLLM2Vec retrieved relevant articles with over 80% accuracy in the top 100 candidates. BioRankLLaMA consistently outperformed baseline rerankers across MAP, MRR, and Precision@5 metrics, confirming the benefit of domain-specific adaptation and contrastive fine-tuning.
DISCUSSION AND CONCLUSION: Our results demonstrate that CiteSure, built on a 2-stage retrieval-augmented generation framework, effectively integrates domain-specific retrieval with LLM-based generation to achieve substantial improvements over baseline approaches. Our work underscores the importance of domain-specific adaptation in biomedical citation recommendation and provides publicly available datasets, models, and code for support future research.