An Overview of Large Language Models for Statisticians
Wenlong Ji, Weizhe Yuan, Emily Getzen, Kyunghyun Cho, Michael I. Jordan, Song Mei, Jason E Weston, Weijie J. Su, Jing Xu, Linjun Zhang
2025-02-26
Summary
This paper talks about how large language models (LLMs), which are advanced AI systems, can benefit from the field of statistics to improve their trustworthiness and transparency while also exploring how LLMs can help statisticians in their work.
What's the problem?
LLMs are powerful tools for tasks like generating text and reasoning, but they face challenges in areas such as understanding uncertainty, making fair decisions, and adapting to different situations. These issues make it harder for people to fully trust and rely on them. At the same time, statisticians have not yet fully explored how LLMs could assist in statistical analysis.
What's the solution?
The researchers suggest that statisticians can help improve LLMs by focusing on areas like uncertainty quantification, fairness, interpretability, and privacy. They also propose ways to use LLMs in statistical tasks to make analyzing data easier and more efficient. By combining the strengths of AI and statistics, they aim to address the weaknesses of current LLMs while advancing both fields.
Why it matters?
This matters because it bridges the gap between AI and statistics, helping to make LLMs more reliable and useful for real-world applications. It also opens up new possibilities for using AI in statistical research, which could lead to better tools for solving complex problems in science, business, and society.
Abstract
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.