Dynamic Retriever Selection in RAG Systems: An RL Approach to User-Centric NLP

This paper investigates a novel use of Reinforcement Learning (RL) to dynamically choose the best retriever in Retrieval-Augmented Generation (RAG) systems with the goal of improving the performance of natural language processing tasks. RAG systems combine retrieval techniques with pre-trained language models to produce responses that are accurate within their context, but a static retriever selection system results in inefficiencies, in a dynamically changing environment where user preferences and the document corpus evolves. We attempt to solve this problem by proposing an RL-based solution to enable dynamic retriever selection based on document context and user feedback. The reinforcement learning agent is able to adjust to user preferences and a changing document corpus by utilizing Q-Learning. The methodology covers issue formulation, agent architecture and training procedures. Our experiments validate our RL-based approach's performance characterized by metrics like user satisfaction and response accuracy. The discussion highlights the strengths and limitations of the Reinforcement Learning approach and suggests future research directions. This experiment underlines the potential of adaptive mechanisms in NLP, showcasing RL's capability to revolutionize RAG applications by creating more responsive and user-tailored NLP systems. (Abstract)