面向统计学家的语言大模型概览

摘要

大型语言模型（LLMs）已成为人工智能（AI）领域的变革性工具，在文本生成、推理和决策制定等多样化任务中展现出卓越能力。尽管其成功主要得益于计算能力和深度学习架构的进步，但在不确定性量化、决策制定、因果推断及分布偏移等领域涌现的新问题，亟需统计学领域的深入参与。本文探讨了统计学家在LLMs发展中可能做出重要贡献的潜在领域，特别是那些旨在增强人类用户信任与透明度的方面。因此，我们聚焦于不确定性量化、可解释性、公平性、隐私保护、水印技术及模型适应等问题。同时，我们也思考了LLMs在统计分析中的可能角色。通过架起AI与统计学之间的桥梁，我们期望促进更深层次的合作，共同推进LLMs的理论基础与实践应用，最终塑造其在应对复杂社会挑战中的角色。

English

Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation, reasoning, and decision-making. While their success has primarily been driven by advances in computational power and deep learning architectures, emerging problems -- in areas such as uncertainty quantification, decision-making, causal inference, and distribution shift -- require a deeper engagement with the field of statistics. This paper explores potential areas where statisticians can make important contributions to the development of LLMs, particularly those that aim to engender trustworthiness and transparency for human users. Thus, we focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation. We also consider possible roles for LLMs in statistical analysis. By bridging AI and statistics, we aim to foster a deeper collaboration that advances both the theoretical foundations and practical applications of LLMs, ultimately shaping their role in addressing complex societal challenges.

面向统计学家的语言大模型概览

An Overview of Large Language Models for Statisticians

摘要

Summary

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