RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons
Score
72提出新的前向传播统计框架，显著降低计算成本，对可解释性方法有实质增量。
包含扰动实验与 token 分布验证，方法论和实验设计较完整。
计算开销降低两个数量级，可将领域级神经元分析推广到大模型场景。
机制可解释性为当前 AI 研究热点，该效率方案具有及时参考意义。
来自 arXiv AI/CL/LG 预印本，学术来源可信度较高，但尚未经同行评审。
Base 80.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
It combines methodological novelty with empirical validation and a significant efficiency advantage, offering high reference value.
AI commentary
「What stands out here is replacing expensive gradient computation with forward-pass statistics, achieving two orders of magnitude efficiency gain—very practical for researchers doing large-scale neuron analysis. If you work on interpretability or alignment, this framework is worth a look.」
Quick overview
Mechanistic interpretability faces the challenge of identifying stable neuron behavior across entire domains. Existing methods often rely on instance-level point estimates or computationally expensive procedures, which obscure population-level variability or limit scalable analysis. RACE proposes a forward-pass statistical framework using residual alignment to estimate functional consistency of Transformer neurons across domains. Perturbation experiments show RACE achieves superior domain specificity compared to gradient-based point estimates, and token-distribution results verify the association between selected neurons and target domains. Computational overhead is two orders of magnitude lower than gradient-based methods.
- RACE uses forward passes and residual alignment to evaluate neuron functional consistency without gradient computation.
- It outperforms gradient-based point estimates in domain specificity and validates neuron-domain associations at the token distribution level.
- Computational overhead is two orders of magnitude lower than gradient-based methods, enabling scalable domain-wide analysis.
Full text
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