Related Experiment Video
Updated: Jul 29, 2025

05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
356
LADER: Log-Augmented DEnse Retrieval for Biomedical Literature Search
Qiao Jin1, Andrew Shin1, Zhiyong Lu1
1National Institutes of Health, Bethesda, Maryland, USA.
Arxiv
|May 19, 2023
Summary
Log-Augmented Dense Retrieval (LADER) enhances biomedical literature search by incorporating click logs from similar queries. This novel approach significantly improves retrieval performance across all query types, setting new state-of-the-art benchmarks.
Area of Science:
- Information Retrieval
- Biomedical Informatics
- Computer Science
Background:
- User search behavior, particularly document clicks, provides valuable signals for improving information retrieval systems.
- Biomedical literature search engines often deal with short queries and a high concentration of clicks on top-ranked documents.
- Existing dense retrieval methods can be further enhanced by leveraging historical user interaction data.
Conclusions:
- LADER offers a simple yet effective method for improving biomedical literature retrieval by leveraging click log data.
- The approach demonstrates substantial performance gains across the spectrum of query frequencies.
- Further integration of user logs is expected to yield even greater improvements in retrieval accuracy.
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