@inproceedings{
hazineh2023linear,
title={Linear Latent World Models in Simple Transformers: A Case Study on Othello-{GPT}},
author={Dean Hazineh and Zechen Zhang and Jeffrey Chiu},
booktitle={Socially Responsible Language Modelling Research},
year={2023},
url={https://openreview.net/forum?id=6mreYNKLKv}
}
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.
Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT
Dean Hazineh Zechen Zhang Jeffrey Chiu