Circuit Tracing: Revealing Computational Graphs in Language Models
The paper presents a novel circuit tracing method to enhance language model interpretability by creating attribution graphs, validating findings through case studies, and suggesting future research directions for advanced models.
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The paper introduces a novel method for understanding the inner workings of language models through a technique called circuit tracing. This approach involves creating graph representations of the model's computations by substituting complex components with a more interpretable "cross-layer transcoder" model. The authors focus on generating "attribution graphs" that illustrate how specific features interact to produce outputs for given prompts. They validate their findings through various case studies, including factual recall and addition tasks, and emphasize the importance of understanding these mechanisms for improving model interpretability. The study also discusses the limitations of current methods and suggests that while the cross-layer transcoder approach incurs initial costs, it ultimately enhances the clarity and simplicity of circuit analysis. The authors conclude by outlining future directions for research, including the application of their methods to more advanced models like Claude 3.5 Haiku.
- The paper presents a method for uncovering the mechanisms of language models using circuit tracing.
- Attribution graphs are created to visualize the interactions of features in the model's computations.
- The study includes case studies to validate the proposed methodology and its effectiveness.
- Limitations of the current approach are acknowledged, with suggestions for future research directions.
- The cross-layer transcoder model improves interpretability despite initial training costs.
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