SAGE: An Agentic Explainer Framework for Interpreting SAE Features in Language Models

This article introduces SAGE, a framework designed to interpret features learned by Sparse Autoencoders (SAEs) within Language Models (LLMs). The use of an 'agentic' approach suggests an attempt to automate or enhance the interpretability process, potentially offering a more nuanced understanding of how LLMs function. The focus on SAEs indicates an interest in understanding the internal representations of LLMs, which is a key area of research for improving model transparency and control.