GitHub - adamkarvonen/chess_llm_interpretability: Evaluating an LLM trained on chess PGN strings using techniques from the Othello World Models paper.

This research demonstrates that large language models trained on chess games encode rich internal representations of board states and player skill levels. By employing linear probes, the study reveals that these models maintain an implicit "world model" of piece positions and rating estimates, allowing researchers to visualize and interpret the model’s reasoning processes during next-token prediction. This capability extends beyond simple pattern matching, indicating a functional understanding of the game’s structural rules and competitive dynamics. Furthermore, the ability to perform interventions on these internal representations offers powerful insights into mechanistic interpretability. By altering the model’s internal state, such as "deleting" pieces from its mental representation, researchers can observe corresponding changes in output, confirming that the model actively reasons about game state rather than relying solely on superficial statistical correlations. This approach provides a concrete method for testing causal relationships within the neural network’s processing layers. This work is highly relevant to the open_data movement as it showcases how accessible, pre-trained models and publicly available datasets can be leveraged to advance the transparency of artificial intelligence. The comprehensive release of code, data, and pre-trained models ensures that these interpretability techniques are reproducible and inclusive. By democratizing access to tools for analyzing model internals, this project encourages community-driven efforts to make AI systems more understandable, trustworthy, and aligned with human expectations in complex strategic domains.

Source: github.com
Published on 2024-01-08

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