Summary of The Computational Complexity Of Circuit Discovery For Inner Interpretability, by Federico Adolfi et al.
The Computational Complexity of Circuit Discovery for Inner Interpretability
by Federico Adolfi, Martina G. Vilas, Todd Wareham
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Complexity (cs.CC); Neurons and Cognition (q-bio.NC)
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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 proposes a conceptual scaffolding to reason about circuit discovery in neural networks using classical and parameterized computational complexity theory. The authors formalize a comprehensive set of queries that capture mechanistic explanation and propose a framework for their analysis. They study the complexity of these queries on multi-layer perceptrons (MLPs) and transformers, revealing a challenging landscape where many queries are NP-hard, Σp2-hard, or inapproximable. However, they also prove that some hard problems can be tackled with better-understood heuristics, and certain queries are tractable or fixed-parameter tractable. This framework allows for understanding the scope and limits of interpretability queries, exploring viable options, and comparing their resource demands among existing and future architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special math to understand how neural networks work. Neural networks are like super powerful computers that can learn from data, but we need to be able to explain why they make certain decisions. The authors came up with a new way to think about this problem by using two types of math: classical and parameterized. They used these ideas to study different ways to ask questions about how neural networks work and found out that some of these questions are really hard to answer, while others can be solved more easily. This is important because it helps us understand what kinds of questions we can ask about neural networks and which ones might require more effort or new techniques. |
