Generalization or Memorization? Brittleness Testing for Chess-Trained Language Models

1. 1Train a language model on chess data using a character-level approach
2. 2Evaluate the model on a benchmark of chess puzzles to assess its performance
3. 3Apply brittleness testing to distinguish between generalization and memorization
4. 4Analyze the results to identify areas where the model excels or fails
5. 5Fine-tune the model to improve its understanding of the game

AI engineers and researchers benefit from this knowledge to improve the robustness of their models, while data scientists can apply these testing methods to other domains

💡 Brittleness testing can reveal whether a model has truly learned the rules of chess or is just relying on memorization

🤖 Can chess-trained language models really play like pros or are they just memorizing moves? 💡