Multi-task retriever fine-tuning for domain-specific and efficient RAG
Patrice Béchard, Orlando Marquez Ayala
2025-01-09
Summary
This paper talks about a new way to make AI systems that use Retrieval-Augmented Generation (RAG) work better and more efficiently for specific topics or industries.
What's the problem?
Current RAG systems have two main issues: 1) They often need to find very specific information, which is hard for general AI models. 2) Using separate systems for each type of information is too expensive and complicated.
What's the solution?
The researchers created a method to train a small part of the AI system (called a retriever) to handle many different types of specific information. They taught this retriever using instructions for various tasks in different fields. This allows one system to do the job of many, making it cheaper and faster to use.
Why it matters?
This matters because it could make AI systems that use RAG much more practical for real-world use. It could help businesses and organizations use AI to find and use specific information more easily and cheaply. This could lead to better AI assistants, more accurate information retrieval, and new ways to use AI in different industries.
Abstract
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when deploying Large Language Models (LLMs), as it can address typical limitations such as generating hallucinated or outdated information.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.