EPISODE · May 24, 2025 · 19 MIN
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
from Best AI papers explained · host Enoch H. Kang
This paper investigates whether Generative Pre-trained Transformer (GPT) models, trained solely for next-token prediction, implicitly learn a causal world model. By proposing a causal interpretation of GPT's attention mechanism, the authors suggest that these models can perform zero-shot causal structure learning for input sequences. Experiments in controlled game environments like Othello and Chess show that GPT is more likely to generate legal moves for out-of-distribution sequences when the attention mechanism strongly encodes a causal structure, highlighting a connection between implicit causal learning and adherence to rules.
What this episode covers
This paper investigates whether Generative Pre-trained Transformer (GPT) models, trained solely for next-token prediction, implicitly learn a causal world model. By proposing a causal interpretation of GPT's attention mechanism, the authors suggest that these models can perform zero-shot causal structure learning for input sequences. Experiments in controlled game environments like Othello and Chess show that GPT is more likely to generate legal moves for out-of-distribution sequences when the attention mechanism strongly encodes a causal structure, highlighting a connection between implicit causal learning and adherence to rules.
NOW PLAYING
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
No transcript for this episode yet
Similar Episodes
Mar 31, 2026 ·54m
Mar 27, 2026 ·14m
Mar 24, 2026 ·42m
Mar 20, 2026 ·42m
Mar 17, 2026 ·41m
Mar 13, 2026 ·44m