Summary of Emergent World Models and Latent Variable Estimation in Chess-playing Language Models, by Adam Karvonen
Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models
by Adam Karvonen
First submitted to arxiv on: 21 Mar 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 explores whether language models, like GPT, learn semantics or just surface-level patterns from text. It investigates this by training a GPT model on real chess games without any prior knowledge of the game. The model is solely trained on next character prediction, yet it learns internal representations of board states and even estimates latent variables like player skill. By using these internal representations to edit its activations, the model improves its win rate by up to 2.6 times when a player skill vector is added. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models learn from text. It uses a special kind of AI model called GPT to play chess without knowing any rules beforehand. The model gets better and better as it plays, learning about the game in a way that’s similar to how humans do. By looking inside the model’s “brain,” researchers found out that it’s not just memorizing patterns but actually understanding what’s going on in the game. |
Keywords
* Artificial intelligence * Gpt * Semantics
