WriteBack-RAG: Training the Knowledge Base through Evidence Distillation and Write-Back

Forum topic · 小凯 · 2026-03-29

Summary

This paper proposes WriteBack-RAG, a framework that treats the knowledge base of retrieval-augmented generation (RAG) systems as a trainable component rather than a static, one-time-assembled resource. Since facts needed to answer queries are often scattered across multiple documents and buried in irrelevant content, the framework uses labeled examples to identify where retrieval succeeds, isolates the relevant documents, and distills them into compact knowledge units that are indexed alongside the original corpus. The authors—Yuxing Lu, Xukai Zhao, Wei Wu, and Jinzhuo Wang—evaluate the approach across four RAG methods, six benchmarks, and two LLM backbones, reporting an average improvement of +2.14%. The paper was posted to arXiv (2603.25737) on March 26, 2026. This work is relevant to researchers and engineers interested in RAG optimization, knowledge base curation, and evidence distillation.

Paper Overview

Field: NLP
Authors: Yuxing Lu, Xukai Zhao, Wei Wu, Jinzhuo Wang
Published: 2026-03-26
arXiv: 2603.25737

Abstract

In retrieval-augmented generation (RAG) systems, the knowledge base is typically assembled once and never updated afterward, even though the facts needed for a query are often scattered across multiple documents and buried in irrelevant content.

This paper argues that the knowledge base should be treated as a trainable component. The authors propose WriteBack-RAG, a framework that:

1. Uses labeled examples to identify where retrieval succeeds; 2. Isolates the relevant documents; 3. Distills them into compact knowledge units; 4. Indexes these units alongside the original corpus.

Results

The framework was evaluated across four RAG methods, six benchmarks, and two LLM backbones, yielding an average gain of +2.14%.

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