Retrieval augmentation is critical when Language Models (LMs) exploit non-parametric knowledge related to the query through external knowledge bases before reasoning. The retrieved information is incorporated into LMs as context alongside the query, enhancing the reliability of responses towards factual questions. Prior researches in retrieval augmentation typically follow a retriever-generator paradigm. In this context, traditional retrievers encounter challenges in precisely and seamlessly extracting query-relevant information from knowledge bases. To address this issue, this paper introduces a novel retrieval augmentation framework called ChatLR that primarily employs the powerful semantic understanding ability of Large Language Models (LLMs) as retrievers to achieve precise and concise information retrieval. Additionally, we construct an LLM-based search and question answering system tailored for the financial domain by fine-tuning LLM on two tasks including Text2API and API-ID recognition. Experimental results demonstrate the effectiveness of ChatLR in addressing user queries, achieving an overall information retrieval accuracy exceeding 98.8\%.

翻译：检索增强对于语言模型在推理前通过外部知识库利用与查询相关的非参数知识至关重要。检索到的信息作为上下文与查询一同输入语言模型，从而提升对事实性问题回答的可靠性。以往的检索增强研究通常遵循检索器-生成器范式。在此背景下，传统检索器在从知识库中精确且无缝地提取查询相关信息方面面临挑战。为解决这一问题，本文提出了一种名为ChatLR的新型检索增强框架，主要利用大语言模型强大的语义理解能力作为检索器，以实现精确简洁的信息检索。此外，我们通过在大语言模型上对Text2API和API-ID识别两项任务进行微调，构建了一个专为金融领域定制的基于大语言模型的搜索与问答系统。实验结果表明，ChatLR在解决用户查询方面效果显著，整体信息检索准确率超过98.8%。