PRCA: Fitting Black-Box Large Language Models for Retrieval Question
  Answering via Pluggable Reward-Driven Contextual Adapter
Haoyan Yang Zhitao Li Yong Zhang Jianzong Wang Ning Cheng Ming Li Jing Xiao
The Retrieval Question Answering (ReQA) task employs the retrieval-augmented
framework, composed of a retriever and generator. The generator formulates the
answer based on the documents retrieved by the retriever. Incorporating Large
Language Models (LLMs) as generators is beneficial due to their advanced QA
capabilities, but they are typically too large to be fine-tuned with budget
constraints while some of them are only accessible via APIs. To tackle this
issue and further improve ReQA performance, we propose a trainable Pluggable
Reward-Driven Contextual Adapter (PRCA), keeping the generator as a black box.
Positioned between the retriever and generator in a Pluggable manner, PRCA
refines the retrieved information by operating in a token-autoregressive
strategy via maximizing rewards of the reinforcement learning phase. Our
experiments validate PRCA's effectiveness in enhancing ReQA performance on
three datasets by up to 20% improvement to fit black-box LLMs into existing
frameworks, demonstrating its considerable potential in the LLMs era.