Retrieval-Augmented Generation (RAG) has become ubiquitous when deploying Large Language Models (LLMs), as it can address typical limitations such as generating hallucinated or outdated information. However, when building real-world RAG applications, practical issues arise. First, the retrieved information is generally domain-specific. Since it is computationally expensive to fine-tune LLMs, it is more feasible to fine-tune the retriever to improve the quality of the data included in the LLM input. Second, as more applications are deployed in the same real-world system, one cannot afford to deploy separate retrievers. Moreover, these RAG applications normally retrieve different kinds of data. Our solution is to instruction fine-tune a small retriever encoder on a variety of domain-specific tasks to allow us to deploy one encoder that can serve many use cases, thereby achieving low-cost, scalability, and speed. We show how this encoder generalizes to out-of-domain settings as well as to an unseen retrieval task on real-world enterprise use cases.

本文解决了检索增强生成(RAG)在实际应用中遇到的识别特定领域信息的问题，同时降低了fine-tune大语言模型的高成本。我们的创新方法在于对小型检索器编码器进行指令微调，使其能够适应多种领域特定任务，从而在不同应用场景中实现低成本、可扩展性和快速响应。实验表明，该编码器在领域外设置和未见检索任务中也能有效推广，具有广泛的实用潜力。

领域特定的多任务检索器微调以实现高效的检索增强生成