AutoMIR: Effective Zero-Shot Medical Information Retrieval without
  Relevance Labels
Authors: name: Lei Li, name: Xiangxu Zhang, name: Xiao Zhou, name: Zheng Liu

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.