Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks
Zhicheng Guo, Sijie Cheng, Yile Wang, Peng Li, Yang Liu · 2023
Retrieval-augmented methods have received increasing attention to support downstream tasks by leveraging useful information from external resources.Recent studies mainly focus on exploring retrieval to solve knowledgeintensive (KI) tasks.However, the potential of retrieval for most non-knowledge-intensive (NKI) tasks remains under-explored.There are two main challenges to leveraging retrievalaugmented methods for NKI tasks: 1) the demand for diverse relevance score functions and 2) the dilemma between training cost and task performance.To address these challenges, we propose a two-stage framework for NKI tasks, named PGRA.In the first stage, we adopt a task-agnostic retriever to build a shared static index and select candidate evidence efficiently.In the second stage, we design a prompt-guided reranker to rerank the nearest evidence according to task-specific relevance for the reader.Experimental results show that PGRA outperforms other state-ofthe-art retrieval-augmented methods.Our analyses further investigate the influence factors to model performance and demonstrate the generality of PGRA.Codes are available at https://github.com/THUNLP-MT/PGRA.