Exploring the Biology of LLMs with Circuit Tracing with Emmanuel Ameisen - #727
Published:Apr 14, 2025 19:40
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode discussing research on the internal workings of large language models (LLMs). Emmanuel Ameisen, a research engineer at Anthropic, explains how his team uses "circuit tracing" to understand Claude's behavior. The research reveals fascinating insights, such as how LLMs plan ahead in creative tasks like poetry, perform calculations, and represent concepts across languages. The article highlights the ability to manipulate neural pathways to understand concept distribution and the limitations of LLMs, including how hallucinations occur. This work contributes to Anthropic's safety strategy by providing a deeper understanding of LLM functionality.
Key Takeaways
- •Circuit tracing is a method for understanding the internal workings of LLMs.
- •LLMs exhibit complex behaviors like planning and cross-lingual concept representation.
- •Understanding LLM internals is crucial for safety and mitigating issues like hallucinations.
Reference
“Emmanuel explains how his team developed mechanistic interpretability methods to understand the internal workings of Claude by replacing dense neural network components with sparse, interpretable alternatives.”