Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the concept of superposition in large language model feature representations in this 47-minute conference talk from Conf42 LLMs 2024. Delve into mechanistic interpretability, neural network representations, and the qualities of these representations. Examine decomposability and linearity in depth, including linear composition as a compression scheme and its demands. Investigate the linear representation puzzle and neuron-feature requirements before diving into the superposition hypothesis. Analyze sparsity and learn techniques for recovering features in superposition. Conclude with a discussion on feature exploration in large language models.