RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions

• URL: http://arxiv.org/abs/2501.00353v1
• Date: Tue, 31 Dec 2024 09:00:51 GMT
• Title: RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
• Authors: Wanlong Liu, Junying Chen, Ke Ji, Li Zhou, Wenyu Chen, Benyou Wang,
• Abstract summary: RAG-Instruct is a general method for synthesizing diverse and high-quality RAG instruction data based on any source corpus.<n>We construct a 40K instruction dataset from Wikipedia, comprehensively covering diverse RAG scenarios and tasks.<n>Experiments demonstrate that RAG-Instruct effectively enhances LLMs' RAG capabilities, achieving strong zero-shot performance.
• Score: 25.952471869592443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face two limitations: (1) they only cover limited RAG scenarios. (2) They suffer from limited task diversity due to the lack of a general RAG dataset. To address these limitations, we propose RAG-Instruct, a general method for synthesizing diverse and high-quality RAG instruction data based on any source corpus. Our approach leverages (1) five RAG paradigms, which encompass diverse query-document relationships, and (2) instruction simulation, which enhances instruction diversity and quality by utilizing the strengths of existing instruction datasets. Using this method, we construct a 40K instruction dataset from Wikipedia, comprehensively covering diverse RAG scenarios and tasks. Experiments demonstrate that RAG-Instruct effectively enhances LLMs' RAG capabilities, achieving strong zero-shot performance and significantly outperforming various RAG baselines across a diverse set of tasks. RAG-Instruct is publicly available at https://github.com/FreedomIntelligence/RAG-Instruct.

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