Do language models plan ahead for future tokens? (2404.00859v2)

Abstract: Do transformers "think ahead" during inference at a given position? It is known transformers prepare information in the hidden states of the forward pass at time step $t$ that is then used in future forward passes $t+\tau$. We posit two explanations for this phenomenon: pre-caching, in which off-diagonal gradient terms present during training result in the model computing features at $t$ irrelevant to the present inference task but useful for the future, and breadcrumbs, in which features most relevant to time step $t$ are already the same as those that would most benefit inference at time $t+\tau$. We test these hypotheses by training LLMs without propagating gradients to past timesteps, a scheme we formalize as myopic training. In a constructed synthetic data setting, we find clear evidence for pre-caching. In the autoregressive LLMing setting, our experiments are more suggestive of the breadcrumbs hypothesis, though pre-caching increases with model scale.

• The paper demonstrates that transformers can pre-cache features when tested on synthetic tasks that require future token prediction.
• The investigation introduces a myopic training method that restricts gradient propagation, effectively differentiating pre-caching from breadcrumbs.
• The study finds that in natural language settings, transformers mainly rely on breadcrumbs, showing minimal benefits from explicit pre-caching.

Examining Pre-Caching and Breadcrumbs in Transformer Models for Language Prediction

Introduction

In the evolving landscape of NLP, the nuanced performance of transformer models often prompts the exploration of their inner workings. A paper by Wilson Wu, John X. Morris, and Lionel Levine explores this field, questioning whether transformers anticipate future tokens during inference and the underlying mechanisms enabling this predictive capability. Two primary hypotheses are proposed: pre-caching and breadcrumbs. Through a blend of synthetic and natural language data experiments, the paper sheds light on the operational dynamics of transformers, contributing to a nuanced understanding of their predictive processes.

Theoretical Framework

The paper introduces a structured approach to differentiate between the concepts of pre-caching and breadcrumbs. Pre-caching suggests transformers compute features at a certain timestep that, while not immediately beneficial, serve future inferences. Conversely, breadcrumbs hypothesize that features vital for current inference naturally benefit future steps without intentional future-oriented computation. The distinction is explored using a novel myopic training method, which restricts gradient propagation to past timesteps, effectively nullifying the model's ability to pre-cache.

Synthetic Data Experiments

A synthetic task is designed to explicitly require pre-caching for success, revealing transformers' capability to adapt their computational strategy towards future needs. The models trained under traditional and myopic schemes exhibit varied performance, underscoring the inherent ability of transformers to engage in pre-caching when the task demands. This finding is pivotal, showcasing that transformers can and do allocate computational resources for future inference when trained traditionally.

Exploring Natural LLMs

In a natural language setting using GPT-2 variants, the examination leans towards the breadcrumbs hypothesis. While a myopic training regime does indicate a minor performance degradation, suggesting a limited role for pre-caching, the observed effect is nominal. This subtly underscores that transformers, when dealing with natural language data, primarily leverage features optimized for immediate next-token prediction, which incidentally align with future prediction needs, aligning with the breadcrumbs hypothesis.

Implications and Future Direction

The differentiation between pre-caching and breadcrumbs has profound implications for our understanding of transformer models in NLP. The insights regarding transformers' operational dynamics can refine future model development and customization strategies, ensuring more efficient and effective models. Furthermore, the paper navigates through an innovative myopic training approach, opening avenues for further exploration in training methodologies that could optimize the balance between present and future token prediction.

Conclusion

The investigation by Wu, Morris, and Levine contributes significantly to the discourse on how transformers process and predict language. With clear evidence supporting pre-caching in synthetic scenarios and a breadcrumbs-like mechanism in natural language processing, the paper enriches our comprehension of the complex interplay between immediate and future token predictions in transformer models. These findings not only advance the theoretical understanding of transformer behavior but also suggest practical pathways for enhancing model performance across various applications in NLP.