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. 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.
• Score: 25.952471869592443
• License:
• 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.

Related papers

• LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs - No Silver Bullet for LC or RAG Routing [70.35888047551643]
  We present LaRA, a novel benchmark specifically designed to rigorously compare RAG and LC LLMs. LaRA encompasses 2,326 test cases across four practical QA task categories and three types of naturally occurring long texts. We find that the optimal choice between RAG and LC depends on a complex interplay of factors, including the model's parameter size, long-text capabilities, context length, task type, and the characteristics of the retrieved chunks.
  arXiv  Detail & Related papers  (2025-02-14T08:04:22Z)
• mR$^2$AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA [78.45521005703958]
  multimodal Retrieval-Augmented Generation (mRAG) is naturally introduced to provide MLLMs with comprehensive and up-to-date knowledge. We propose a novel framework called textbfRetrieval-textbfReftextbfAugmented textbfGeneration (mR$2$AG) which achieves adaptive retrieval and useful information localization. mR$2$AG significantly outperforms state-of-the-art MLLMs on INFOSEEK and Encyclopedic-VQA
  arXiv  Detail & Related papers  (2024-11-22T16:15:50Z)
• RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards [78.74923079748521]
  Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) Current approaches use instruction tuning to optimize LLMs, improving their ability to utilize retrieved knowledge. We propose a Differentiable Data Rewards ( DDR) method, which trains RAG systems by aligning data preferences between different RAG modules.
  arXiv  Detail & Related papers  (2024-10-17T12:53:29Z)
• Toward General Instruction-Following Alignment for Retrieval-Augmented Generation [63.611024451010316]
  Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. We propose VIF-RAG, the first automated, scalable, and verifiable synthetic pipeline for instruction-following alignment in RAG systems.
  arXiv  Detail & Related papers  (2024-10-12T16:30:51Z)
• RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation [54.707460684650584]
  Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG) RAGLAB is a modular and research-oriented open-source library that reproduces 6 existing algorithms and provides a comprehensive ecosystem for investigating RAG algorithms.
  arXiv  Detail & Related papers  (2024-08-21T07:20:48Z)
• BERGEN: A Benchmarking Library for Retrieval-Augmented Generation [26.158785168036662]
  Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. Inconsistent benchmarking poses a major challenge in comparing approaches and understanding the impact of each component in the pipeline. In this work, we study best practices that lay the groundwork for a systematic evaluation of RAG and present BERGEN, an end-to-end library for reproducible research standardizing RAG experiments.
  arXiv  Detail & Related papers  (2024-07-01T09:09:27Z)
• RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems [0.0]
  Retrieval-Augmented Generation (RAG) has become a standard architectural pattern for domain-specific knowledge into user-facing chat applications. We introduce RAGBench: the first comprehensive, large-scale RAG benchmark dataset of 100k examples. We formalize the TRACe evaluation framework: a set of explainable and actionable RAG evaluation metrics applicable across all RAG domains.
  arXiv  Detail & Related papers  (2024-06-25T20:23:15Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.