AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2410.20050v2)

Abstract: 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 \textbf{S}elf-\textbf{L}earning \textbf{Hy}pothetical \textbf{D}ocument \textbf{E}mbeddings (\textbf{SL-HyDE}) to tackle this issue. SL-HyDE leverages 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 HyDE in retrieval accuracy while showcasing strong generalization and scalability across various LLM and retriever configurations. Our code and data are publicly available at: https://github.com/ll0ruc/AutoMIR

• The paper presents a novel SL-HyDE method that generates hypothetical pseudo-documents to enable zero-shot medical information retrieval without labeled data.
• It employs a self-learning mechanism to iteratively refine retrieval accuracy, achieving a 4.9% improvement in NDCG@10 on the CMIRB benchmark.
• The study demonstrates system scalability and potential applicability across diverse medical domains, reducing reliance on costly relevance labels.

Summary of "AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels"

The paper introduces AutoMIR with SL-HyDE, a novel methodology for zero-shot medical information retrieval (MIR), which operates without the need for relevance-labeled data. The paper addresses the significant challenges inherent in dense retrieval within the medical domain, especially the scarcity of labeled training data, by leveraging hypothetical document embeddings generated through LLMs.

Key Contributions and Methodology

The primary contribution of this research is the development of a Self-Learning Hypothetical Document Embedding (SL-HyDE) framework. This framework utilizes the potential of LLMs to generate hypothetical pseudo-documents in response to a given query, which are then used to inform dense retrieval models. The pseudo-documents are iteratively refined through a self-learning mechanism in which the retrieval model identifies the most relevant real-world documents using unannotated medical corpora.

SL-HyDE achieves this through its innovative adaptability, hypothecating on the retrieved documents to progressively improve both document generation and document retrieval strategies. During training, the generated hypothetical documents provide pseudo-labels that allow the retrieval system to enhance its encoding of medical concepts without explicit supervised signals.

Chinese Medical Information Retrieval Benchmark (CMIRB)

An essential part of the paper is the introduction of the CMIRB, a benchmark created to evaluate the efficacy of MIR systems in realistic medical contexts. Comprising five tasks and ten datasets, CMIRB serves as a comprehensive evaluation framework, exposing systems to challenges involving real-world medical scenarios. By benchmarking ten models, the CMIRB establishes a rigorous standard for assessing various retrieval architectures and strategies in the medical domain.

Results and Performance

Extensive experiments conducted on CMIRB demonstrate that SL-HyDE notably surpasses existing methods in terms of retrieval accuracy and showcases robust scalability across different configurations of LLMs and retrievers. For instance, the SL-HyDE method significantly improves over the baseline HyDE combination, evidenced by a 4.9% uplift in NDCG@10 score across multiple tasks. The paper further highlights that SL-HyDE's self-learning strategy allows it to commence with entirely unlabeled medical corpora, effectively circumventing the traditional dependency on costly labeled datasets.

Implications and Future Directions

The implications of this research extend to both practical applications and theoretical advancements in zero-shot MIR systems. Practically, SL-HyDE provides an adaptable solution for retrieving diverse medical information without necessitating extensive annotation, thus offering a scalable framework applicable to various LLMs and retrieval models. Theoretically, this work opens avenues to explore the broader potential of self-learning mechanisms in modeling complex knowledge domains without conventional supervision.

For future developments, it suggests expanding the framework to incorporate more nuanced configurations and challenging benchmarks, potentially incorporating multi-modal data to further enhance the retrieval performance across diverse medical sub-domains. Additionally, the paper points to the possibility of exploiting similar methods in other specialized domains, where the scarcity of labeled data often curtails model performance.

In conclusion, this paper significantly advances the field of medical information retrieval by demonstrating an effective framework to overcome the limitations of data scarcity, paving the way for future innovations within automated, efficient retrieval systems across the medical landscape.