Papers by Roy Mayan

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Challenge:	Existing pipelines for describing concepts in large language models (LLMs) rely on activating inputs to generate descriptions that fail to capture the causal effect of a feature on outputs.
Approach:	They propose to use inputs that activate features to generate natural language descriptions for concepts represented by large language models (LLMs) . identifying activating inputs is costly, and the mechanistic role of a feature in model behavior is determined by how inputs cause it to activate and how feature activation affects outputs.
Outcome:	The proposed methods capture the causal effect of a feature on outputs better than input-centric descriptions, but combine the two leads to the best performance on both input and output evaluations.