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

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arxiv 2406.13629 v3 pith:WSINC3R3 submitted 2024-06-19 cs.CL cs.LG

Zhepei Wei , Wei-Lin Chen , Yu Meng This is my paper
classification cs.CLcs.LG
keywords denoisinginstructraggenerationrationalesretrievedaccuracyanswerschallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Powerless Noise: How Experimental Settings Shape the Reported Power of Noise

    cs.IR 2026-07 accept novelty 6.5 of 10

    The Power-of-Noise effect in RAG is reproducible only under the original constrained setup and disappears or weakens once instruction templates, longer outputs, and modern LLMs are used.

  2. MedJudgeRAG: Option-Wise Evidence Judgment with Dynamic Knowledge Graphs for Medical MCQA

    cs.IR 2026-07 conditional novelty 6.0 of 10

    MedJudgeRAG fine-tunes a medical MCQA model to emit per-option evidence verdicts and choose grounded, elimination, or parametric reasoning, improving over vanilla RAG by up to 17 accuracy points.

  3. HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A hierarchical chain-of-thought instruction-tuning curriculum for filtering, combination, and reasoning improves zero-shot retrieval-augmented QA.

  4. DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction

    cs.CL 2025-06 conditional novelty 6.0 of 10

    DeRAGEC explicitly denoises retrieved named-entity candidates with phonetic scores, definitions, and synthetic rationales, improving ASR error-correction WER and NE hit ratio without additional training.

  5. Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A configurable benchmark with four retrieval-noise types shows RAG accuracy drops sharply beyond 50% noise and that noise type, not just quantity, determines failure patterns.

  6. SmartRAG: Native Graph-Based RAG for Mobile Device

    cs.AI 2026-07 conditional novelty 5.0 of 10

    SmartRAG shows a 1.7B-parameter smartphone model, backed by a continuously updatable knowledge graph, can match or exceed a 32B cloud LLM on several multi-hop QA benchmarks while using bounded memory.

  7. GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Framework

    cs.CL 2025-08 reject novelty 5.0 of 10

    GOSU globally merges semantic units from text chunks into a unit-centric knowledge graph and uses three-tier keyword retrieval to improve RAG generation quality, according to LLM-judge win rates.

  8. CrEst: Credibility Estimation for Contexts in LLMs via Weak Supervision

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A label-free method that scores retrieved documents by their agreement with the majority in embedding space and uses those scores to filter context in LLM question answering.

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