Transformer Circuits: Reverse-Engineering AI Models | Generated by AI
Transformer Circuits is a research publication platform focused on mechanistic interpretability in transformer-based language models. It hosts a collaborative series of technical papers, blog posts, and analyses from Anthropic’s interpretability team, aiming to reverse-engineer how these AI models work at a granular level—breaking down neural networks into interpretable “circuits” to understand emergent behaviors like induction heads or factual recall.
Key Highlights:
- Main Theme: Demystifying transformers by studying toy models, scaling laws, and real-world examples (e.g., GPT-2). It’s part of the broader push for AI safety through transparency.
- Notable Content:
- “A Mathematical Framework for Transformer Circuits” (introductory paper).
- Deep dives into specific mechanisms, like attention patterns or modular architectures.
- Updates on ongoing research, with code, datasets, and visualizations shared via GitHub.
- Creators: Primarily led by Anthropic researchers like Chris Olah, Neel Nanda, and others in the interpretability field.
If you’re into AI alignment or ML internals, it’s a goldmine for hands-on insights.