---
id: 20260929-T0-04
title: "TRACE微调：解决RAG中参数知识与检索上下文冲突"
title_en: "TRACE Fine-Tuning Resolves RAG Parametric-Retrieval Conflicts"
url: https://ai.daily.yangsir.net/daily/20260929-T0-04
issue_date: 2026-09-29
publish_date: 2026-09-28T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2609.30337
---

# TRACE微调：解决RAG中参数知识与检索上下文冲突

一篇新论文提出TRACE微调方法，用于解决检索增强生成（RAG）中检索知识与模型内部参数知识冲突的问题。当外部检索到的知识与模型已有知识矛盾时，模型容易产生错误输出。TRACE通过微调让模型更好地处理这种冲突，提升RAG的鲁棒性。对构建RAG系统的开发者来说，这意味着在知识库频繁更新的场景下，模型能更可靠地使用外部信息而非固执己见。

## English Version

**TRACE Fine-Tuning Resolves RAG Parametric-Retrieval Conflicts**

A new paper proposes TRACE fine-tuning to address conflicts between retrieved knowledge and a model's internal parametric knowledge in Retrieval-Augmented Generation (RAG). When external retrieved knowledge contradicts what the model already knows, it can produce incorrect outputs. TRACE fine-tunes the model to better handle such conflicts, improving RAG robustness. For developers building RAG systems, this means more reliable use of external information when knowledge bases are frequently updated.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2609.30337)

**详情页**：https://ai.daily.yangsir.net/daily/20260929-T0-04

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