文章背景与核心概要
在大语言模型（LLM）的机械可解释性研究中，理解单个神经元在不同数据领域中的行为表现一直是一个重大挑战。当前的方法通常依赖于实例级的点估计（无法捕捉总体层面的变异性），或是计算开销巨大的繁琐流程，这严重阻碍了大规模分析的开展。
为此，作者引入了一种名为 RACE（Residual Alignment for Consistency Estimation，用于一致性估计的残差对齐） 的全新前向传播统计框架，旨在评估 Transformer 神经元在整个领域中的功能一致性。
RACE 框架的核心亮点包括： * 卓越的领域特异性： 在识别稳定神经元行为方面，优于传统的基于梯度的点估计方法。 * 领域关联性： 通过标记分布层面的分析，验证了特定神经元与目标领域之间的联系。 * 高效性： 与现有的基于梯度的方法相比，计算开销降低了两个数量级。
RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons
arXiv: 2608.24758
Subject: Computer Science > Artificial Intelligence (cs.AI)
Date: August 25, 2026
Venue: EMNLP-26 Main Conference
arXiv: 2608.24758
Subject: Computer Science > Artificial Intelligence (cs.AI)
Date: August 25, 2026
Venue: EMNLP-26 Main Conference
Summary
In the field of mechanistic interpretability, understanding how individual neurons behave across diverse data domains remains a significant hurdle. Current methodologies often rely on instance-level point estimates—which fail to capture population-level variability—or computationally intensive procedures that hinder large-scale analysis.
The authors introduce RACE (Residual Alignment for Consistency Estimation), a novel forward-pass statistical framework designed to evaluate the functional consistency of Transformer neurons across entire domains.
Key highlights of the RACE framework include: * Superior Domain Specificity: Outperforms traditional gradient-based point estimates in identifying stable neuron behavior. * Domain Association: Validates the link between specific neurons and target domains through token-distribution-level analysis. * High Efficiency: Reduces computational overhead by two orders of magnitude compared to existing gradient-based methods.
Summary
In the field of mechanistic interpretability, understanding how individual neurons behave across diverse data domains remains a significant hurdle. Current methodologies often rely on instance-level point estimates—which fail to capture population-level variability—or computationally intensive procedures that hinder large-scale analysis.
The authors introduce RACE (Residual Alignment for Consistency Estimation), a novel forward-pass statistical framework designed to evaluate the functional consistency of Transformer neurons across entire domains.
Key highlights of the RACE framework include: * Superior Domain Specificity: Outperforms traditional gradient-based point estimates in identifying stable neuron behavior. * Domain Association: Validates the link between specific neurons and target domains through token-distribution-level analysis. * High Efficiency: Reduces computational overhead by two orders of magnitude compared to existing gradient-based methods.
Authors
- Runyu Wang
- Bo Liu
- Xiaxin Zhang
- Yu Han
- Jiawei Cao
- Xiaoye Zhang
- Zhe Zhang
- Yifan Yang
- Peng Ping
Authors
- Runyu Wang
- Bo Liu
- Xiaxin Zhang
- Yu Han
- Jiawei Cao
- Xiaoye Zhang
- Zhe Zhang
- Yifan Yang
- Peng Ping
Access & Resources
- PDF: View Paper
- HTML: Experimental Version
- Source: TeX Source
- DOI: 10.48550/arXiv.2608.24758
Access & Resources
- PDF: View Paper
- HTML: Experimental Version
- Source: TeX Source
- DOI: 10.48550/arXiv.2608.24758
License
Creative Commons Attribution 4.0 International
License