Summary of Transformers Use Causal World Models in Maze-solving Tasks, by Alex F. Spies et al.
Transformers Use Causal World Models in Maze-Solving Tasks
by Alex F. Spies, William Edwards, Michael I. Ivanitskiy, Adrians Skapars, Tilman Räuker, Katsumi Inoue, Alessandra Russo, Murray Shanahan
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 explores the inner workings of transformer models trained on maze-solving tasks, discovering that these networks naturally develop highly structured representations referred to as “World Models” (WMs). The authors use Sparse Autoencoders and attention patterns to examine the construction of WMs and demonstrate consistency between feature-based and circuit-based analyses. They find it easier to activate features than to suppress them, and models can reason about mazes involving more simultaneously active features than they encountered during training. However, when these same mazes are provided as input tokens, the models fail. The authors also show that positional encoding schemes influence how World Models are structured within the model’s residual stream. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at what happens inside transformer models when they’re trained to solve mazes. It finds that these models naturally create special types of representations called “World Models”. Researchers used a tool called Sparse Autoencoders and looked at how the model focuses on certain parts of the maze to understand how World Models work. They found it’s easier for the model to learn new things than to forget old ones, and they can even solve mazes that are more complicated than what they learned from before. However, if the model is given a simplified version of the maze, it doesn’t know how to solve it. The authors also discovered that the way they set up the model’s position in space affects how well it works. |
Keywords
» Artificial intelligence » Attention » Positional encoding » Transformer
