Data-driven Circuit Discovery for Interpretability of Language Models
Circuit discovery aims to explain how language models (LMs) implement a specific task by localizing and interpreting a circuit, a computational subgraph responsible for the LM's behavior. Existing circuit discovery methods are hypothesis-driven; they first informally define a task with a dataset, and then apply a circuit discovery algorithm over that dataset to obtain a single circuit. This imposes two strong assumptions: that the LM implements the task with a single circuit, and that the datase
Record details
Published: 9 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (9 May 2026), “Data-driven Circuit Discovery for Interpretability of Language Models,” evidence record 4652, https://ethics.ai/record/4652 (originally published by arXiv).
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