Multi-task retriever fine-tuning for domain-specific and efficient RAG

2025-01-08

AI Summary

Retrieval-augmented generation (RAG) faces efficiency bottlenecks in balancing domain-specific retrieval accuracy with the scalability of shared retrievers across diverse tasks. Method: We propose a multitask instruction-driven unified fine-tuning paradigm for lightweight retrievers, built upon compact encoders. Our approach integrates instruction tuning, joint multitask training, and domain-adaptive representation learning—simultaneously optimizing in-domain accuracy, cross-domain generalization, and zero-shot transfer to unseen tasks—thereby overcoming the scalability limitations of conventional single-task fine-tuning. Contribution/Results: Evaluated across over ten heterogeneous enterprise RAG tasks, our single unified model supports multilingual, multi-domain, and multimodal retrieval, achieving 12–28% higher accuracy and 40% lower inference latency compared to task-specific fine-tuned baselines. To our knowledge, this is the first work to enable efficient, instruction-driven adaptation of a general-purpose retriever, establishing a new paradigm for low-cost, flexible, and scalable RAG deployment.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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when deploying Large Language Models (LLMs), as it can address typical limitations such as generating hallucinated or outdated information.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.

Problem

• RAG Optimization
• Cost Reduction
• Multi-Application Adaptability

Innovation

• Adaptable Retrievers
• Retrieval-Augmented Generation (RAG) Systems
• Cross-Domain Adaptation