Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?
TL;DR
Interpretability methods often evaluate features in isolation, which may not reflect their real-world interactions. This study introduces a multi-concept evaluation framework to assess how well methods like sparse autoencoders (SAEs) disentangle concepts such as sentiment and tense.
✦ Why It Matters
Engineers should adopt multi-concept evaluations to better understand feature interactions in interpretability methods.
Key Takeaways
- Evaluate interpretability methods using multi-concept frameworks.
- Recognize that features may overlap in concept representation.
- Use steering techniques cautiously, as they can affect multiple concepts.
- Rely on more than correlation metrics to assess feature selectivity.
How It Works
The study employs a multi-concept evaluation framework to assess how well different featurization methods, like sparse autoencoders, can isolate and manipulate specific concepts. By analyzing the interactions between features and concepts, the researchers demonstrate that features often respond to multiple concepts, complicating the interpretation of neural network outputs.