Summary of Transformers Represent Belief State Geometry in Their Residual Stream, by Adam S. Shai et al.
Transformers represent belief state geometry in their residual stream
by Adam S. Shai, Sarah E. Marzen, Lucas Teixeira, Alexander Gietelink Oldenziel, Paul M. Riechers
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents evidence that large language models are built with a specific computational structure when trained on next-token prediction tasks. The authors argue that this structure is a result of the meta-dynamics of belief updating over hidden states of the data-generating process, which is linearly represented in the residual stream of transformers. The study investigates cases where the belief state geometry is represented in the final residual stream or distributed across multiple layers, providing a framework to explain these observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores what happens when we train large language models on next-token prediction tasks. It shows that these models develop a special structure that helps them make predictions about future data. The researchers use a mathematical theory called optimal prediction to understand how this structure works and why it’s important. They also demonstrate that the model can learn information about future data, even if it’s not explicitly trained on it. |
Keywords
» Artificial intelligence » Token
