AutoMIR: Effective Zero-Shot Medical Information Retrieval without
  Relevance Labels
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.
6722e59578218659f678a559
Lei Li
6722e59578218659f678a55a
Xiangxu Zhang
6722e59578218659f678a55b
Xiao Zhou
6722e59578218659f678a55c
Zheng Liu
extracted_pending
2024-10-31T02:04:07.533Z
64a38c590111d5ff6c3d5f2b
/avatars/ef13dc7ce243819bc0da9b04e778b432.svg
zhengliu
user
zl101
AutoMIR: Effective Zero-Shot Medical Information Retrieval without
  Relevance Labels
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.
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.
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)