English(EN) From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution

新方法通过重写响应来增强LLM训练数据归因

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员引入了一种名为“影响引导响应重写”的新方法，以改进大型语言模型（LLM）的训练数据归因（TDA）。该技术利用影响函数识别有影响力的训练样本，然后重写它们的响应以使其符合期望的行为，这种方法被证明比简单地重加权同一批样本更有效。研究表明，与传统的重加权相比，响应重写即使在安全相关任务中也能在LLM中产生更强、更一致的行为转变。 AI

影响这项研究通过改进训练数据对模型输出的影响方式，可能带来更有效的方法来理解和控制LLM的行为。

排序理由该集群包含一篇详细介绍LLM训练数据归因新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yuzhang Luo, Chenpeng Wang, Jianhui Chen, Liangming Pan · 2026-09-03 04:00

从重加权到重写：解锁训练数据归因中影响性样本的干预效应

arXiv:2609.02771v1 Announce Type: cross Abstract: Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which examples are selected and how they are modified. Influence functions (IF) estimate beha…

本文如何被排名

报道来源 [1]

从重加权到重写：解锁训练数据归因中影响性样本的干预效应

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