DO LARGE LANGUAGE MODELS (REALLY) NEED STATISTICAL FOUNDATIONS?

• 成果类型: Article
• 署名作者: Su, Weijie
• 署名单位: University of Pennsylvania
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/26-AOAS2151
• 发表日期: 2026-03
• 页码: 724-743
• 关键词: large language models; statistical foundations; statistical models; black-box models; prediction

摘要

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 highlight the importance of timely engagement by our statistics commu