Seonglae ChoCrossCoder (2024)
with Cross fine-tuning model & scaling transferability by diffing within same architecture
Sparse Crosscoders for Cross-Layer Features and Model Diffing
This note will cover some theoretical examples motivating crosscoders, and then present preliminary experiments applying them to cross-layer superposition and model diffing. We also briefly discuss the theory of how crosscoders might simplify circuit analysis, but leave results on this for a future update.
https://transformer-circuits.pub/2024/crosscoders/index.html
Open Source Replication of Anthropic’s Crosscoder paper for model-diffing — LessWrong
Intro Anthropic recently released an exciting mini-paper on crosscoders (Lindsey et al.). In this post, we open source a model-diffing crosscoder tra…
https://www.lesswrong.com/posts/srt6JXsRMtmqAJavD/open-source-replication-of-anthropic-s-crosscoder-paper-for
SAE Transferability
SAEs (usually) Transferability Between Base and Chat Models
SAEs (usually) Transfer Between Base and Chat Models — LessWrong
This is an interim report sharing preliminary results that we are currently building on. We hope this update will be useful to related research occur…
https://www.lesswrong.com/posts/fmwk6qxrpW8d4jvbd/saes-usually-transfer-between-base-and-chat-models
Transfer Learning across layers
By leveraging shared representations between adjacent layers, training costs and time can be significantly reduced by applying transfer learning instead of training Sparse AutoEncoder (SAE) from scratch. Backward was better than forward, which can be understood as starting with prior knowledge of computation results.
- forward SAE
- backward SAE