Is It Even Possible to Understand Quantum Mechanics? Common sense is of no help in studying reality at the atomic scale. A big problem with physics is that it's invented by humans. We see stuff around us and use our observations to build a mental model of the world. Children do this, and of course physicists keep right on doing it.

The success of today's large language models (LLMs) depends on the observation that larger models perform better. However, the origin of this neural scaling law, that loss decreases as a power law with model size, remains unclear. We propose that representation superposition, meaning that LLMs represent more features than they have dimensions, can be a key contributor to loss and cause neural scaling. Based on Anthropic's toy model, we use weight decay to control the degree of superposition, allowing us to systematically study how loss scales with model size. When superposition is weak, the loss follows a power law only if data feature frequencies are power-law distributed.

This paper introduces the Manifold Probe, a supervised method for discovering representation manifolds in superposition. The method generalizes linear regression probes by learning the space of features of a concept that can be linearly predicted from the representations, and then learning the directions used to encode them. We demonstrate the probe on representations of time and space in Llama 2-7b, finding manifolds which linearly represent an interpretable set of features in each case. In the case of time, we show that by steering along the manifold, we can influence the model's completions about the years in which famous songs, movies and books were released, providing evidence that the Manifold Probe can discover manifolds which are causally involved in model behaviour.

Both of the techniques assume bitwise access to the oracle as a classical function.

Finally, we provide mathematical bounds on the interference between superposition channels in MIMOFormer.

We show how to "compile" human-readable programs into standard decoder-only transformer models.

This view enables a structured variational approximation capturing dependencies across variables in the model.

Neural Information Processing Systems http://nips.cc/

This paper presents a text classification algorithm inspired by the notion of superposition of states in quantum physics.

The rates for representing the class of functionsGD via DReLU layers is sharpuptoconstants,asshownbymatchinglowerbounds.