InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales

2024-06-19

AI Summary

To address factual degradation in retrieval-augmented generation (RAG) caused by retriever bias or corpus noise, this paper proposes a self-supervised denoising framework requiring no human annotations. Methodologically, it leverages large language models to autonomously synthesize reasoning chains, explicitly modeling document credibility and logical consistency; integrates instruction-guided retrieval understanding, contrastive learning–driven reasoning chain distillation, and multi-granularity document fusion for end-to-end reasoning denoising. The framework supports both zero-shot in-context learning and supervised fine-tuning, significantly enhancing interpretability and verifiability. Empirically, it achieves an average 8.3% improvement over state-of-the-art methods across five knowledge-intensive benchmarks. It demonstrates robustness to scaling the number of retrieved documents and strong cross-domain generalization capability.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Retrieval-augmented generation (RAG) has shown promising potential to enhance the accuracy and factuality of language models (LMs). However, imperfect retrievers or noisy corpora can introduce misleading or even erroneous information to the retrieved contents, posing a significant challenge to the generation quality. Existing RAG methods typically address this challenge by directly predicting final answers despite potentially noisy inputs, resulting in an implicit denoising process that is difficult to interpret and verify. On the other hand, the acquisition of explicit denoising supervision is often costly, involving significant human efforts. In this work, we propose InstructRAG, where LMs explicitly learn the denoising process through self-synthesized rationales -- First, we instruct the LM to explain how the ground-truth answer is derived from retrieved documents. Then, these rationales can be used either as demonstrations for in-context learning of explicit denoising or as supervised fine-tuning data to train the model. Compared to standard RAG approaches, InstructRAG requires no additional supervision, allows for easier verification of the predicted answers, and effectively improves generation accuracy. Experiments show InstructRAG consistently outperforms existing RAG methods in both training-free and trainable scenarios, achieving a relative improvement of 8.3% over the best baseline method on average across five knowledge-intensive benchmarks. Extensive analysis indicates that InstructRAG scales well with increased numbers of retrieved documents and consistently exhibits robust denoising ability even in out-of-domain datasets, demonstrating strong generalizability.

Problem

• Improves accuracy of language models using retrieval-augmented generation.
• Reduces noise from imperfect retrievers or noisy corpora in RAG.
• Enhances interpretability and verification of generated answers.

Innovation

• Self-synthesized rationales for explicit denoising
• No additional supervision required for training
• Improved accuracy and robustness in generation