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Summary of How to Use and Interpret Activation Patching, by Stefan Heimersheim et al.


How to use and interpret activation patching

by Stefan Heimersheim, Neel Nanda

First submitted to arxiv on: 23 Apr 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: None

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
This paper provides a comprehensive summary of best practices for using activation patching, a popular technique for mechanistic interpretability in deep learning models. The authors share their experience with applying this method in practice, highlighting the subtleties involved and offering guidance on how to interpret results effectively. By discussing different application approaches and pitfalls associated with metric choice, the paper aims to equip readers with a deeper understanding of activation patching’s capabilities and limitations.
Low GrooveSquid.com (original content) Low Difficulty Summary
Activation patching is a way to understand what deep learning models are doing. This technique helps us see inside the model’s “brain” to figure out why it makes certain predictions or decisions. The authors of this paper have used activation patching before and want to share their tips and tricks for getting good results. They explain different ways to use this technique and how to make sense of what you find.

Keywords

» Artificial intelligence  » Deep learning