Overview of the Mechanistic Interpretability Field
Автор: Balagansky N.N.
Журнал: Труды Московского физико-технического института @trudy-mipt
Рубрика: Математика
Статья в выпуске: 3 (67) т.17, 2025 года.
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Mechanistic interpretability is an emerging area of AI safety research that aims to understand neural networks at the level of their internal mechanisms. Rather than treating models as black boxes, this approach dissects individual components—neurons, attention heads, and circuits—to identify how specific computations are performed. This paper presents a comprehensive overview of key methods, case studies, and current challenges in the field. The goal is to build tools and frameworks that make the inner workings of large models transparent, thereby increasing trust and safety in AI systems.
Mechanistic interpretability, neural networks, AI safety, model transparency, circuits, interpretability tools
Короткий адрес: https://sciup.org/142245841
IDR: 142245841 | УДК: 004.89