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

Code

• github.com/deanhazineh/emergent-world-representations-othello
  OfficialIn paperpytorch★ 9
• github.com/alxndrtl/othello_mamba
  pytorch★ 49

Abstract

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. We have made the code public.

Tasks

Reproductions