arXiv Machine Learning By Joakim Edin, Casper L. Christensen, R\'obert Csord\'as, Tuukka Ruotsalo, Zhengxuan Wu, Maria Maistro, Jing Huang, Lars Maal{\o}e

Correcting Gradient-Based Circuit Localization via Interaction-Aware Backpropagation

Read the original on arXiv Machine Learning →

arXiv:2505. 17630v4 Announce Type: replace-cross Abstract: Circuit localization methods aim to identify the subset of model components responsible for specific behaviors in large language models, enabling detailed mechanistic analysis.

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arXiv AI
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S^3martCirc: Self-supervised Smart Circuit Discovery

S^3martCirc is a self‑supervised framework that jointly discovers and interprets neural circuits in large language models, rather than treating circuit discovery and functional interpretation as separate stages. It abstracts node behavior into two general computational roles that generalize across tasks and introduces a quantitative metric for assigning these roles, enabling simultaneous identification of importance and function. Experiments demonstrate that S^3martCirc outperforms existing methods in circuit discovery.

By Wendy Zheng, Yinhan He, Liang Wu, Jundong Li
arXiv AI
Sep 21

Decoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models

Fine‑tuning reshapes internal representations of large language models, affecting attention patterns and layer‑wise activations. The study shows that components identified by EAP as important for task performance cluster in specific layers, yet these layers do not align with those undergoing the largest representational changes. Additionally, overlapping EAP components across different tasks do not guarantee cross‑task transfer and can even degrade performance when tasks differ in nature.

By Lingfang Li, Procheta Sen, Shubham Das, Danushka Bollegala