Reward-RAG: Enhancing RAG with Reward Driven Supervision

TL;DR

Reward-RAG introduces a reward-driven supervision method that enhances retrieval-augmented generation by employing a reward model to improve domain-specific relevance and quality of generated responses.

Contribution

It presents a novel Reward-RAG approach that uses CriticGPT to train a reward model, improving RAG's performance across various domains through domain-specific fine-tuning.

Findings

Significant performance improvements on multiple benchmarks.

Enhanced relevance and quality of generated responses.

Effective domain adaptation of RAG models.

Abstract

In this paper, we introduce Reward-RAG, a novel approach designed to enhance the Retrieval-Augmented Generation (RAG) model through Reward-Driven Supervision. Unlike previous RAG methodologies, which focus on training language models (LMs) to utilize external knowledge retrieved from external sources, our method adapts retrieval information to specific domains by employing CriticGPT to train a dedicated reward model. This reward model generates synthesized datasets for fine-tuning the RAG encoder, aligning its outputs more closely with human preferences. The versatility of our approach allows it to be effectively applied across various domains through domain-specific fine-tuning. We evaluate Reward-RAG on publicly available benchmarks from multiple domains, comparing it to state-of-the-art methods. Our experimental results demonstrate significant improvements in performance, highlighting the effectiveness of Reward-RAG in improving the relevance and quality of generated responses. These findings underscore the potential of integrating reward models with RAG to achieve superior outcomes in natural language generation tasks.