Summary of Towards Unifying Interpretability and Control: Evaluation Via Intervention, by Usha Bhalla et al.
Towards Unifying Interpretability and Control: Evaluation via Intervention
by Usha Bhalla, Suraj Srinivas, Asma Ghandeharioun, Himabindu Lakkaraju
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel approach to understanding and controlling large language models is presented. The paper introduces the concept of intervention as a fundamental goal of interpretability, alongside standardized evaluation metrics. By unifying four popular interpretability methods into an encoder-decoder framework, the authors enable interventions on interpretable features that can be mapped back to latent representations to control model outputs. Two new evaluation metrics are introduced: intervention success rate and coherence-intervention tradeoff. The findings reveal that current methods allow for intervention but their effectiveness is inconsistent across features and models. Lens-based methods outperform SAEs and probes in achieving simple, concrete interventions. However, mechanistic interventions often compromise model coherence, underperforming simpler alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at doing things like understanding human language. But it’s hard to understand how they make decisions and control what they do. Some people want to use these models to help us learn or communicate better, but we need ways to make sure they’re working correctly. Researchers have come up with different methods to explain why a model makes certain choices, but these methods often focus on either understanding the model’s thinking or controlling its behavior. The problem is that there isn’t a standard way to evaluate how well these methods work. To fix this, scientists are proposing new ways to measure whether an explanation method can help control what the model does. They’re also trying out different approaches to see which ones work best. |
Keywords
» Artificial intelligence » Encoder decoder
