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    <abstract>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 knowledge-intensive (KI) tasks. However, the potential of retrieval for most non-knowledge-intensive (NKI) tasks remains under-explored. There are two main challenges to leveraging retrieval-augmented 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-of-the-art retrieval-augmented methods. Our analyses further investigate the influence factors to model performance and demonstrate the generality of PGRA. The code and model will be released for further research.</abstract>
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%0 Conference Proceedings
%T Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks
%A Guo, Zhicheng
%A Cheng, Sijie
%A Wang, Yile
%A Li, Peng
%A Liu, Yang
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F guo-etal-2023-prompt
%X 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 knowledge-intensive (KI) tasks. However, the potential of retrieval for most non-knowledge-intensive (NKI) tasks remains under-explored. There are two main challenges to leveraging retrieval-augmented 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-of-the-art retrieval-augmented methods. Our analyses further investigate the influence factors to model performance and demonstrate the generality of PGRA. The code and model will be released for further research.
%R 10.18653/v1/2023.findings-acl.693
%U https://aclanthology.org/2023.findings-acl.693
%U https://doi.org/10.18653/v1/2023.findings-acl.693
%P 10896-10912

Markdown (Informal)

[Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks](https://aclanthology.org/2023.findings-acl.693) (Guo et al., Findings 2023)

• Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks (Guo et al., Findings 2023)

ACL

• Zhicheng Guo, Sijie Cheng, Yile Wang, Peng Li, and Yang Liu. 2023. Prompt-Guided Retrieval Augmentation for Non-Knowledge-Intensive Tasks. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10896–10912, Toronto, Canada. Association for Computational Linguistics.