Summary of Learning the Latent Rules Of a Game From Data: a Chess Story, by Ben Fauber
Learning the Latent Rules of a Game from Data: A Chess Story
by Ben Fauber
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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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 demonstrates that small generative language models with millions of parameters can learn the rules of a process from data associated with it. Inspired by Stefan Zweig’s “Schachnovelle,” 28M and 125M parameter pretrained foundational small language models (SLMs) are fine-tuned to propose legal moves, solve chess problems, and learn the rules of chess. The impact of fine-tuning epochs on outcomes is explored, showing reductions in model hallucinations with more examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how small language models can learn chess rules from data. It’s like a game where these AI models get better at playing chess by looking at lots of chess moves and problems. They even start to make good moves themselves! This is cool because it helps us understand how AI can learn new things. |
Keywords
» Artificial intelligence » Fine tuning
