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Workshop: Socially Responsible Language Modelling Research (SoLaR)

Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT

Zechen Zhang · Dean Hazineh · Jeffrey Chiu


Foundation models exhibit significant capabilities in decision-making and logical deductions. Nonetheless, a continuing discourse persists regarding their genuine understanding of the world as opposed to mere stochastic mimicry. This paper meticulously examines a simple transformer trained for Othello, extending prior research to enhance comprehension of the emergent world model of Othello-GPT. The investigation reveals that Othello-GPT encapsulates a linear representation of opposing pieces, a factor that causally steers its decision-making process. This paper further elucidates the interplay between the linear world representation and causal decision-making, and their dependence on layer depth and model complexity.

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