title: AutoMIR: Effective Zero-Shot Medical Information Retrieval without
    Relevance Labels
summary: Medical information retrieval (MIR) is essential for retrieving relevant
  medical knowledge from diverse sources, including electronic health records,
  scientific literature, and medical databases. However, achieving effective
  zero-shot dense retrieval in the medical domain poses substantial challenges
  due to the lack of relevance-labeled data. In this paper, we introduce a novel
  approach called Self-Learning Hypothetical Document Embeddings (SL-HyDE) to
  tackle this issue. SL-HyDE leverages large language models (LLMs) as generators
  to generate hypothetical documents based on a given query. These generated
  documents encapsulate key medical context, guiding a dense retriever in
  identifying the most relevant documents. The self-learning framework
  progressively refines both pseudo-document generation and retrieval, utilizing
  unlabeled medical corpora without requiring any relevance-labeled data.
  Additionally, we present the Chinese Medical Information Retrieval Benchmark
  (CMIRB), a comprehensive evaluation framework grounded in real-world medical
  scenarios, encompassing five tasks and ten datasets. By benchmarking ten models
  on CMIRB, we establish a rigorous standard for evaluating medical information
  retrieval systems. Experimental results demonstrate that SL-HyDE significantly
  surpasses existing methods in retrieval accuracy while showcasing strong
  generalization and scalability across various LLM and retriever configurations.
  CMIRB data and evaluation code are publicly available at:
  https://github.com/CMIRB-benchmark/CMIRB.
paper_authors._id: 6722e59578218659f678a559
paper_authors.name: Lei Li
paper_authors._id: 6722e59578218659f678a55a
paper_authors.name: Xiangxu Zhang
paper_authors._id: 6722e59578218659f678a55b
paper_authors.name: Xiao Zhou
paper_authors._id: 6722e59578218659f678a55c
paper_authors.name: Zheng Liu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-10-31T02:04:07.533Z
paper_authors._id: 64a38c590111d5ff6c3d5f2b
paper_authors.avatarUrl: /avatars/ef13dc7ce243819bc0da9b04e778b432.svg
paper_authors.fullname: zhengliu
paper_authors.type: user
paper_authors.user: zl101
paper_title: AutoMIR: Effective Zero-Shot Medical Information Retrieval without
    Relevance Labels
paper_summary: Medical information retrieval (MIR) is essential for retrieving relevant
  medical knowledge from diverse sources, including electronic health records,
  scientific literature, and medical databases. However, achieving effective
  zero-shot dense retrieval in the medical domain poses substantial challenges
  due to the lack of relevance-labeled data. In this paper, we introduce a novel
  approach called Self-Learning Hypothetical Document Embeddings (SL-HyDE) to
  tackle this issue. SL-HyDE leverages large language models (LLMs) as generators
  to generate hypothetical documents based on a given query. These generated
  documents encapsulate key medical context, guiding a dense retriever in
  identifying the most relevant documents. The self-learning framework
  progressively refines both pseudo-document generation and retrieval, utilizing
  unlabeled medical corpora without requiring any relevance-labeled data.
  Additionally, we present the Chinese Medical Information Retrieval Benchmark
  (CMIRB), a comprehensive evaluation framework grounded in real-world medical
  scenarios, encompassing five tasks and ten datasets. By benchmarking ten models
  on CMIRB, we establish a rigorous standard for evaluating medical information
  retrieval systems. Experimental results demonstrate that SL-HyDE significantly
  surpasses existing methods in retrieval accuracy while showcasing strong
  generalization and scalability across various LLM and retriever configurations.
  CMIRB data and evaluation code are publicly available at:
  https://github.com/CMIRB-benchmark/CMIRB.
paper_ai_summary: A novel self-learning approach called SL-HyDE uses large language models to generate hypothetical documents for zero-shot medical information retrieval, outperforming existing methods in accuracy and scalability.
paper_ai_keywords: Self-Learning Hypothetical Document Embeddings (SL-HyDE) large language models (LLMs) zero-shot dense retrieval medical information retrieval medical domain pseudo-document generation Chinese Medical Information Retrieval Benchmark (CMIRB)