EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 29·5 min read
Canonical-basis realignment for Transformer LLMs: every hidden axis becomes independently measurable and controllable
TL;DR
A new method called Canonical Basis for Language Models (CBLL) allows for a lossless transformation of Transformer LLMs, enabling independent measurement of each hidden axis. This breakthrough enhances interpretability and control over model behavior.
✦ Why It Matters
Implement CBLL to enhance the interpretability and control of your Transformer models today.
Key Takeaways
- Use CBLL for independent measurement of hidden axes in LLMs.
- Explore axis ablation to understand model dependencies and performance.
- Apply lossless realignment techniques to maintain model behavior.
- Leverage findings to enhance interpretability of Transformer models.
How It Works
CBLL employs a lossless affine transformation that rotates the model's coordinate system, allowing each hidden axis to correspond to specific spectral directions of the model's weight matrices. This transformation enables independent measurement of axes, revealing phenomena like the bipolar oscillator and respiration patterns that were previously obscured in the standard basis.