Data-driven Circuit Discovery for Interpretability of Language Models
📰 ArXiv cs.AI
Learn how to apply data-driven circuit discovery for interpreting language models and uncovering their computational subgraphs
Action Steps
- Apply circuit discovery algorithms to a dataset to identify computational subgraphs
- Use data-driven methods to localize and interpret circuits in language models
- Evaluate the effectiveness of circuit discovery in explaining model behavior
- Compare the results of data-driven circuit discovery with hypothesis-driven approaches
- Refine the circuit discovery process based on the results and insights gained
Who Needs to Know This
NLP researchers and engineers can benefit from this technique to improve the interpretability of their language models, while data scientists can apply it to understand how their models make predictions
Key Insight
💡 Data-driven circuit discovery can help uncover the computational subgraphs responsible for a language model's behavior, improving its interpretability
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🤖 Discover how data-driven circuit discovery can improve interpretability of language models! #NLP #Interpretability
Key Takeaways
Learn how to apply data-driven circuit discovery for interpreting language models and uncovering their computational subgraphs
Full Article
Title: Data-driven Circuit Discovery for Interpretability of Language Models
Abstract:
arXiv:2605.09129v1 Announce Type: new Abstract: 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
Abstract:
arXiv:2605.09129v1 Announce Type: new Abstract: 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
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