Summary of Challenges in Mechanistically Interpreting Model Representations, by Satvik Golechha et al.
Challenges in Mechanistically Interpreting Model Representations
by Satvik Golechha, James Dao
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Mechanistic interpretability (MI) aims to reverse-engineer AI models by understanding the algorithms neural networks learn. Previous MI studies focused on trivial behaviors, but more important capabilities for safety and trust require studying hidden representations inside networks as the unit of analysis. We formalize representation units, highlight their importance, and evaluate them in Mistral-7B-Instruct-v0.1. Our exploratory study shows that representations are crucial but challenging to study using established MI methods, highlighting the need for new frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) models can be tricky to understand. One way to do this is by “reverse-engineering” what they learn. So far, most research has focused on simple things that AI models can do. But there are more important things AI models need to know, like how to make safe decisions and build trust. We want to study these hidden ideas inside the AI models. This helps us understand what’s really going on in the models’ “minds.” Our early findings show that these hidden ideas are crucial but hard to study using current methods. |
