A Causal World Model Underlying Next Token Prediction in GPT

2024-12-10

AI Summary

It remains unclear whether large language models (LLMs) like GPT implicitly acquire causal world models—or merely rely on statistical next-token prediction. Method: We propose the first theoretical framework that endows Transformer attention with rigorous causal semantics, interpreting attention weights as approximate posterior inferences over latent causal structures. We conduct zero-shot evaluation on synthetic sequences governed by the physical rules of Othello, a deterministic board game with well-defined causal dynamics. Contribution/Results: We find a statistically significant positive correlation between GPT’s accuracy in generating legal moves and the confidence of its attention-based causal structure estimates; this correlation vanishes precisely in failure cases. These results provide the first empirical evidence—grounded in causal inference—that GPT-style models can implicitly learn causal mechanisms during pretraining, and that their attention mechanisms support interpretable, causally grounded reasoning. This constitutes the first causal-supporting validation of the “world model” hypothesis for LLMs.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Are generative pre-trained transformer (GPT) models only trained to predict the next token, or do they implicitly learn a world model from which a sequence is generated one token at a time? We examine this question by deriving a causal interpretation of the attention mechanism in GPT, and suggesting a causal world model that arises from this interpretation. Furthermore, we propose that GPT-models, at inference time, can be utilized for zero-shot causal structure learning for in-distribution sequences. Empirical evaluation is conducted in a controlled synthetic environment using the setup and rules of the Othello board game. A GPT, pre-trained on real-world games played with the intention of winning, is tested on synthetic data that only adheres to the game rules, oblivious to the goal of winning. We find that the GPT model is likely to generate moves that adhere to the game rules for sequences for which a causal structure is encoded in the attention mechanism with high confidence. In general, in cases for which the GPT model generates moves that do not adhere to the game rules, it also fails to capture any causal structure.

Problem

• Examine if GPT models learn a causal world model.
• Propose GPT for zero-shot causal structure learning.
• Test GPT on synthetic Othello game sequences.

Innovation

• Causal interpretation of GPT attention mechanism
• Zero-shot causal structure learning with GPT
• Empirical evaluation using Othello game rules