So evaluating SAEs - determining which SAE is better at creating the most unique features while being as sparse as possible at the same time - is a very complex topic that is very much at the heart of the current research into LLM interpretability through SAEs.

Assuming you already solved the problem of finding multiple perfect SAE architectures and you trained them to perfection (very much an interesting ML engineering problem that this SAE project attempts to solve) then deciding on which SAE is better comes down to which SAE performs better on the metrics of your automated interpretability methodology. Particularly OpenAI's methodology emphasizes this automated interpretability at scale utilizing a lot of technical metrics upon which the SAEs can be scored _and thereby evaluated_.

Since determining the best metrics and methodology is such an open research question that I could've experimented on for a few additional months, have I instead opted for a simple approach in this first release. I am talking about my and OpenAI's methodology and the differences between both in chapter 4. Interpretability Analysis [1] in my Implementation Details & Results section. I can also recommend reading the OpenAI paper directly or visiting Anthropics transformer-circuits.pub website that often publishes smaller blog posts on exactly this topic.

[1] https://github.com/PaulPauls/llama3_interpretability_sae#4-i... [2] https://transformer-circuits.pub/