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Anthropic has open-sourced a novel interpretability framework for tracing the internal reasoning processes of large language models (LLMs) through attribution graphs, which map the causal relationships between input features and model outputs [1]. This tool, developed in collaboration with Decode Research and led by Anthropic Fellows, provides researchers with three core functionalities: (1) circuit tracing, enabling the generation of attribution graphs for models like Gemma-2-2b and Llama-3.2-1b; (2) interactive visualization via a frontend on Neuronpedia, allowing users to annotate, share, and explore graphs; and (3) hypothesis testing, where modifications to input features can be analyzed for their impact on model behavior [1].
The framework supports the study of complex model behaviors, such as multi-step reasoning and multilingual representation learning, with example analyses provided in a public demo notebook [1]. Attribution graphs are generated using a combination of gradient-based and activation-based attribution methods, though the exact algorithmic details remain unspecified in the provided context [1]. Anthropic emphasizes that the tools are designed to work with both open-weights and proprietary models, though the latter may require additional licensing or access [1]. The company invites the research community to contribute by identifying new circuits for analysis, with unexplored attribution graphs available on Neuronpedia as a starting point [1].
Anthropic’s CEO, Dario Amodei, highlighted the importance of interpretability research, stating that current understanding of AI’s inner workings lags behind model capabilities [1]. By open-sourcing these tools, the company aims to accelerate progress in model transparency and safety, while also fostering collaborative improvements to the technology [1].
https://www.anthropic.com/research/open-source-circuit-tracing
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