Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking

• URL: http://arxiv.org/abs/2505.19722v1
• Date: Mon, 26 May 2025 09:10:19 GMT
• Title: Distilling Closed-Source LLM's Knowledge for Locally Stable and Economic Biomedical Entity Linking
• Authors: Yihao Ai, Zhiyuan Ning, Weiwei Dai, Pengfei Wang, Yi Du, Wenjuan Cui, Kunpeng Liu, Yuanchun Zhou,
• Abstract summary: We propose RPDR'', a framework combining closed-source LLMs and open-source LLMs for re-ranking candidates retrieved by a retriever fine-tuned with a small amount of data.<n>We evaluate RPDR on two datasets, including one real-world dataset and one publicly available dataset involving two languages: Chinese and English.
• Score: 10.436941992571981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical entity linking aims to map nonstandard entities to standard entities in a knowledge base. Traditional supervised methods perform well but require extensive annotated data to transfer, limiting their usage in low-resource scenarios. Large language models (LLMs), especially closed-source LLMs, can address these but risk stability issues and high economic costs: using these models is restricted by commercial companies and brings significant economic costs when dealing with large amounts of data. To address this, we propose ``RPDR'', a framework combining closed-source LLMs and open-source LLMs for re-ranking candidates retrieved by a retriever fine-tuned with a small amount of data. By prompting a closed-source LLM to generate training data from unannotated data and fine-tuning an open-source LLM for re-ranking, we effectively distill the knowledge to the open-source LLM that can be deployed locally, thus avoiding the stability issues and the problem of high economic costs. We evaluate RPDR on two datasets, including one real-world dataset and one publicly available dataset involving two languages: Chinese and English. RPDR achieves 0.019 Acc@1 improvement and 0.036 Acc@1 improvement on the Aier dataset and the Ask A Patient dataset when the amount of training data is not enough. The results demonstrate the superiority and generalizability of the proposed framework.

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