Distributed Representations: Composition & Superposition

This post expands on the concept of superposition in LLMs by introducing and contrasting it with 'composition' in distributed representations. It uses examples from neuroscience to illustrate how these two ideas can be understood as separate dimensions, rather than a single spectrum. The authors propose that composition relates to how distinct features are represented by distinct neurons or groups of neurons, while superposition relates to how multiple features can be encoded within the same neuron or group of neurons. The post highlights a fundamental tension between these two concepts, suggesting a trade-off in their application.