BiomedRAG：一种面向生物医学领域的检索增强大语言模型
BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine
AI总结：
BiomedRAG通过将分块检索文档直接输入LLM，并利用LLM监督检索模型，在5项生物医学NLP任务、9个以上数据集上取得优越性能。
摘要：
大语言模型（LLMs）已迅速成为生物医学和医疗健康领域多种应用的重要资源；然而，这些模型会遇到生成不准确信息或产生幻觉等问题。检索增强生成为这些模型更新知识并提升性能提供了一种解决方案。与以往利用专门交叉注意力机制来帮助LLM编码检索文本的检索增强语言模型不同，BiomedRAG采用了一种更简单的方法，直接将基于分块的检索文档输入到LLM中。这种直接的设计易于应用于现有的检索模型和语言模型，能够有效绕过检索文档中的噪声信息，尤其是在噪声密集型任务中。此外，我们展示了在生物医学领域利用LLM监督检索模型的潜力，使其能够检索到有助于语言模型改进预测的文档。我们的实验表明，借助经过调优的评分器，BiomedRAG在5项生物医学NLP任务上取得了优越性能，涵盖信息抽取（三元组抽取、关系抽取）、文本分类、链接预测和问答，使用了超过9个数据集。例如，在三元组抽取任务中，BiomedRAG在GIT和ChemProt语料库上分别以81.42和88.83的micro-F1分数优于其他三元组抽取系统。
英文摘要：
Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains; however, these models encounter issues such as generating inaccurate information or hallucinations. Retrieval-augmented generation provided a solution for these models to update knowledge and enhance their performance. In contrast to previous retrieval-augmented LMs, which utilize specialized cross-attention mechanisms to help LLM encode retrieved text, BiomedRAG adopts a simpler approach by directly inputting the retrieved chunk-based documents into the LLM. This straightforward design is easily applicable to existing retrieval and language models, effectively bypassing noise information in retrieved documents, particularly in noise-intensive tasks. Moreover, we demonstrate the potential for utilizing the LLM to supervise the retrieval model in the biomedical domain, enabling it to retrieve the document that assists the LM in improving its predictions. Our experiments reveal that with the tuned scorer,\textsc{ BiomedRAG} attains superior performance across 5 biomedical NLP tasks, encompassing information extraction (triple extraction, relation extraction), text classification, link prediction, and question-answering, leveraging over 9 datasets. For instance, in the triple extraction task, \textsc{BiomedRAG} outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
