Do Large Language Models (Really) Need Statistical Foundations?

TL;DR

This paper argues that statistical foundations are crucial for understanding and improving large language models, emphasizing their inherent statistical nature and the need for statistical approaches due to their complexity and black-box characteristics.

Contribution

It presents two main arguments for the importance of statistical methods in LLMs and outlines key research areas where statistics can contribute significantly.

Findings

LLMs are inherently statistical models due to data dependency and stochastic processes.

Statistical approaches are essential for handling LLMs' complexity and black-box nature.

Statistical research will form a diverse mosaic of topics in LLM development.

Abstract

Large language models (LLMs) represent a new paradigm for processing unstructured data, with applications across an unprecedented range of domains. In this paper, we address, through two arguments, whether the development and application of LLMs would genuinely benefit from foundational contributions from the statistics discipline. First, we argue affirmatively, beginning with the observation that LLMs are inherently statistical models due to their profound data dependency and stochastic generation processes, where statistical insights are naturally essential for handling variability and uncertainty. Second, we argue that the persistent black-box nature of LLMs -- stemming from their immense scale, architectural complexity, and development practices often prioritizing empirical performance over theoretical interpretability -- renders closed-form or purely mechanistic analyses generally intractable, thereby necessitating statistical approaches due to their flexibility and often demonstrated effectiveness. To substantiate these arguments, the paper outlines several research areas -- including alignment, watermarking, uncertainty quantification, evaluation, and data mixture optimization -- where statistical methodologies are critically needed and are already beginning to make valuable contributions. We conclude with a discussion suggesting that statistical research concerning LLMs will likely form a diverse ``mosaic'' of specialized topics rather than deriving from a single unifying theory, and highlighting the importance of timely engagement by our statistics community in LLM research.