This review discusses the current frontier of mechanistic interpretability, which aims to understand the computational mechanisms underlying neural networks. While the field has made progress, many open problems remain, including the need for improved methods, better applications to specific goals, and engagement with socio-technical challenges.
January 26, 2025
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