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Updated: Aug 13, 2026
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
A unified framework and benchmark for generalizable biomedical knowledge extraction and applications with large
Wuyang Lan1, Siqi Zhang2, Wenzheng Wang1
1State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China; School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Abstract:
Biomedical information extraction (BIE) is fundamental for transforming unstructured biomedical text into structured, computable knowledge, yet the effectiveness of large language models (LLMs) remains limited by dataset heterogeneity and lack of unified benchmarks. We present InfoFlowEX, a unified framework for generalizable biomedical knowledge extraction with LLMs. InfoFlowEX incorporates an automated data integration pipeline using ontology-guided alignment to construct BIE-Corpus, a large-scale multi-domain benchmark unifying 40 public datasets for named entity recognition and relation extraction. We further introduce a task-conditioned schema instruction tuning strategy encoding 28 biomedical entity and relation types into a schema codebase, enabling LLMs to align heterogeneous annotations and generalize across settings. Finally, we evaluated InfoFlowEX in diverse applications, including evidence retrieval for question-answering, clinical diagnosis from electronic health records, and knowledge graph expansion. Results demonstrate that InfoFlowEX equips LLMs with robust adaptability, achieving consistent gains over baselines with minimal task-specific customization, highlighting InfoFlowEX for real-world biomedical applications.
