Summary of Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms, by Michael Hanna et al.
Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms
by Michael Hanna, Sandro Pezzelle, Yonatan Belinkov
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 The paper introduces a new method for finding minimal computational subgraphs, or circuits, in language models (LMs) that better maintain faithfulness. Faithfulness means that a circuit can be ablated without changing the model’s performance on a task. The authors improve upon previous methods like Edge Attribution Patching (EAP), which uses gradient-based approximations to interventions. Their new method, EAP with Integrated Gradients (EAP-IG), demonstrates higher node overlap and faithfulness compared to previous approaches. This work highlights the importance of measuring faithfulness when using circuits to compare mechanisms models use to solve tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how language models make decisions. They want to find small parts of these models that are responsible for a specific task, like answering questions. Right now, we have ways to do this, but they’re not perfect. The researchers introduce a new method that does better than the current methods at finding these important parts and making sure they actually work together correctly. This is important because it helps us understand how language models think and make decisions. |
