Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization.

. We propose RePAIR, a response-action learning paradigm that directly maps flawed RAG outputs to error-mitigating action plans without relying on fine-grained error taxonomies and explicit critic supervision. Across multiple benchmarks, RePAIR consistently improves agentic RAG performance.

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Publication Details

Journal
PubMed
Published
2026-07-01
Primary Topic
Domain Adaptation and Few-Shot Learning
Type
article
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article

Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization.

Yifan Peng, Chunhua Weng, Gongbo Zhang
PubMed
Domain Adaptation and Few-Shot Learning
article

Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization.

Yifan Peng, Chunhua Weng, Gongbo Zhang
article en

Abstract

. We propose RePAIR, a response-action learning paradigm that directly maps flawed RAG outputs to error-mitigating action plans without relying on fine-grained error taxonomies and explicit critic supervision. Across multiple benchmarks, RePAIR consistently improves agentic RAG performance.

PubMedVol. 2026
Cornell University (US), Weill Cornell Medicine (US), Columbia University (US)
Openalex Percentile: Top 5%
Domain Adaptation and Few-Shot Learning
OpenAlex
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