SAEExplainer

SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization

arXiv:2606.08496v1 Announce Type: new Abstract: Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features still remains a central challenge. Current explanation methods, however, typically operate within an open-loop paradigm, failing to leverage mechanistic feedback for further refinement. In this paper, we propose SAEExplainer, a training framework utilizes activation...