Unveiling Language Skills via Path-Level Circuit Discovery

• URL: http://arxiv.org/abs/2410.01334v2
• Date: Mon, 16 Dec 2024 03:33:36 GMT
• Title: Unveiling Language Skills via Path-Level Circuit Discovery
• Authors: Hang Chen, Jiaying Zhu, Xinyu Yang, Wenya Wang,
• Abstract summary: We propose a novel path-level circuit discovery framework capturing how behaviors emerge through interconnected linear chain.<n>Our framework is constructed upon a fully-disentangled linear combinations of memory circuits'' decomposed from the original model.<n>In contrast to circuit graph from existing works, we focus on the complete paths of a generic skill rather than on the fine-grained responses to individual components of the input.
• Score: 31.608080868988825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Circuit discovery with edge-level ablation has become a foundational framework for mechanism interpretability of language models. However, its focus on individual edges often overlooks the sequential, path-level causal relationships that underpin complex behaviors, thus potentially leading to misleading or incomplete circuit discoveries. To address this issue, we propose a novel path-level circuit discovery framework capturing how behaviors emerge through interconnected linear chain and build towards complex behaviors. Our framework is constructed upon a fully-disentangled linear combinations of ``memory circuits'' decomposed from the original model. To discover functional circuit paths, we leverage a 2-step pruning strategy by first reducing the computational graph to a faithful and minimal subgraph and then applying causal mediation to identify common paths of a specific skill, termed as skill paths. In contrast to circuit graph from existing works, we focus on the complete paths of a generic skill rather than on the fine-grained responses to individual components of the input. To demonstrate this, we explore three generic language skills, namely Previous Token Skill, Induction Skill and In-Context Learning Skill using our framework and provide more compelling evidence to substantiate stratification and inclusiveness of these skills.

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