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The Retrieval Question Answering (ReQA) task employs the retrieval-augmentedframework, composed of a retriever and generator. The generator formulates theanswer based on the documents retrieved by the retriever. Incorporating LargeLanguage Models (LLMs) as generators is beneficial due to their advanced QAcapabilities, but they are typically too large to be fine-tuned with budgetconstraints while some of them are only accessible via APIs. To tackle thisissue and further improve ReQA performance, we propose a trainable PluggableReward-Driven Contextual Adapter (PRCA), keeping the generator as a black box.Positioned between the retriever and generator in a Pluggable manner, PRCArefines the retrieved information by operating in a token-autoregressivestrategy via maximizing rewards of the reinforcement learning phase. Ourexperiments validate PRCA's effectiveness in enhancing ReQA performance onthree datasets by up to 20% improvement to fit black-box LLMs into existingframeworks, demonstrating its considerable potential in the LLMs era.

PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter