Summary of Activation Scaling For Steering and Interpreting Language Models, by Niklas Stoehr et al.
Activation Scaling for Steering and Interpreting Language Models
by Niklas Stoehr, Kevin Du, Vésteinn Snæbjarnarson, Robert West, Ryan Cotterell, Aaron Schein
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The paper explores the idea of steering language models to correct incorrect predictions by modifying specific activation vectors. The authors argue that this is crucial for understanding a model’s internal workings. They propose a three-term objective: effectiveness (flipping the correct and incorrect tokens), faithfulness (leaving other tokens unaffected), and minimality (using only activation scaling). Using gradient-based optimization, they learn and evaluate an efficient and interpretable intervention method that modifies the signed magnitude of activation vectors to strengthen, weaken, or reverse steering directions. The authors test this approach on synthetic tasks, comparing its performance with traditional steering vectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a special technique to help language models make better predictions. It’s like giving the model a nudge in the right direction when it makes a mistake. The researchers want to understand how language models work inside their “heads” and came up with three rules: fix the mistake, don’t mess with other parts of the model, and only use a little bit of effort. They used a special way to adjust some numbers inside the model to make this happen. It worked pretty well on simple tests, but they’re still figuring out how to apply it to more complex tasks. |
Keywords
» Artificial intelligence » Optimization
