Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task

Investigating Internal Representations of Language Models through Othello-GPT

Introduction

This paper delineates an incisive exploration into the internal representations formed by language models during sequence generation tasks. Focusing specifically on a simplified synthetic environment, the research examines whether a language model, devoid of external knowledge, can develop internal state representations that are instrumental in generating predictions. The paper employs Othello, a straightforward board game, as the testbed and adapts a variant of the GPT model—termed Othello-GPT—to predict legal moves based solely on game transcripts.

Methods

Game Environment and Datasets

To investigate internal representations, the authors use Othello, an 8x8 board game, where two players alternately place black or white discs to maximize their respective counts. The environment is meticulously chosen for its blend of simplicity and sufficient complexity to avoid mere memorization.

Two datasets are employed: a "championship" dataset sourced from expert human games, and a "synthetic" dataset consisting of a vast number of randomly generated legal moves. The championship dataset embodies strategic depth, while the synthetic dataset ensures extensive coverage of valid move sequences.

Model Architecture and Training

Othello-GPT is trained autoregressively to predict the next move in a sequence, with tokens representing board tiles. An 8-layer GPT model with an 8-head attention mechanism and a 512-dimensional hidden space is utilized. The model, initialized randomly, learns purely from sequence information without any predefined rules, representing a pure test of the emergent capabilities of sequence models.

Results

Emergent Competence in Predicting Legal Moves

The Othello-GPT demonstrates impressive proficiency in predicting legal moves. For the synthetic dataset, the error rate was a mere 0.01%, while for the championship dataset, it was 5.17%. These figures strongly suggest that the model is learning beyond pure sequence memorization.

Internal Representations Examined with Probes

The study employs probe techniques to scrutinize the model's internal states, attempting to correlate them with the actual game board states. Nonlinear probes achieved notably lower error rates compared to linear probes, suggesting that the board state representation within Othello-GPT is inherently nonlinear.

Interventional Experiments

To validate the causal significance of these representations, interventional experiments were conducted. The technique involved altering internal activations to correspond with alternative board states and observing the resultant changes in move predictions. The interventions consistently led to prediction changes aligned with the modified board states, reinforcing the hypothesis that the internal board representations have a causal impact on the model's decisions.

Interpretation Tools: Latent Saliency Maps

The authors implemented "latent saliency maps" as an interpretability tool. These maps visualize the contribution of specific board tiles to the model's predictions. The synthetic dataset's maps highlighted only the tiles necessary for legal moves, while the championship dataset's maps revealed more complex patterns, indicative of strategic considerations. This visual differentiation underscores the effectiveness of the latent saliency maps in elucidating model behavior.

Discussion and Implications

The study's findings offer significant insights into the nature of internal representations in language models trained on sequence tasks. The ability of Othello-GPT to develop nonlinear, causally effective internal states merely through sequential data is noteworthy. Practically, these insights can influence the design of more interpretable AI systems, where understanding internal representations is crucial for explainability and reliability.

The authors speculate that the techniques and insights from this controlled, synthetic setting could extend to more complex, natural-language environments. Future research might leverage similar probing and intervention strategies to dissect representations in models trained on varied linguistic and non-linguistic tasks.

Conclusion

This paper meticulously demonstrates that language models, when tasked with predicting legal moves in a simplified game environment like Othello, develop intricate internal representations that embody the game’s state. Through non-linear probing, interventional experiments, and latent saliency map visualizations, the study provides compelling evidence of these models' emergent competency. These explorations pave the way for more nuanced examinations of interior mechanisms in AI, fostering advancements in model interpretability and control.