統計學家視角下的大型語言模型綜述

An Overview of Large Language Models for Statisticians

February 25, 2025

作者: Wenlong Ji, Weizhe Yuan, Emily Getzen, Kyunghyun Cho, Michael I. Jordan, Song Mei, Jason E Weston, Weijie J. Su, Jing Xu, Linjun Zhang

cs.AI

摘要

大型語言模型（LLMs）已成為人工智慧（AI）領域中的變革性工具，在文本生成、推理和決策等多樣化任務中展現出卓越的能力。雖然其成功主要得益於計算能力和深度學習架構的進步，但在不確定性量化、決策制定、因果推理和分佈偏移等新興問題領域，需要更深入地結合統計學的專業知識。本文探討了統計學家在LLMs發展中可能做出重要貢獻的潛在領域，特別是那些旨在增強人類用戶信任度和透明度的方面。因此，我們聚焦於不確定性量化、可解釋性、公平性、隱私保護、數字水印和模型適應等問題。同時，我們也考慮了LLMs在統計分析中的可能角色。通過橋接AI與統計學，我們旨在促進更深層次的合作，以推進LLMs的理論基礎和實際應用，最終塑造其在應對複雜社會挑戰中的角色。

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.