用生成式重排序提升生物医学实体链接的效率与准确率
BeLink: Biomedical Entity Linking Meets Generative Re-Ranking
- 对生成模型进行集合指令微调，用于实体链接中的候选重排
- 在多个基准上提升准确率3%-24%，推理速度更快
- 适用于需要高效部署的医疗文本分析场景
尽管近期取得进展，基于大语言模型的生物医学实体链接（BEL）仍存在计算效率低、难以实际部署的问题。本文表明，在贝尔流程的重排序阶段使用开源生成模型进行指令微调可有效解决该问题。我们提出一种集合式指令微调方法，实现快速且精准的候选选择。该方法在多个贝尔基准上表现优异，链接准确率提升3%-24%，同时相比当前最优方法显著降低推理时间。我们将生成式重排序器集成至BeLink系统中，该系统为模块化、端到端设计，专为实际生物医学文本应用而构建。
原文摘要 · Abstract (English)
Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy in practical settings. In this work, we demonstrate that instruction-tuning of open-source generative models can offer an effective solution when applied at the re-ranking stage of the BEL pipeline. We propose a set-wise instruction-tuning formulation that enables fast and accurate candidate selection. Our method demonstrates strong performance on multiple BEL benchmarks, yielding significant improvements in linking accuracy (3%-24%) while reducing inference time compared to the state-of-the-art. We integrate our generative re-ranker into BeLink, a modular, end-to-end system designed for practical real-world BEL applications.
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