An Overview of Large Language Models for Statisticians
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.
67bf50aa9a1df81dba235650
Wenlong Ji
67bf50aa9a1df81dba235651
Weizhe Yuan
67bf50aa9a1df81dba235652
Emily Getzen
67bf50aa9a1df81dba235653
Kyunghyun Cho
67bf50aa9a1df81dba235654
Michael I. Jordan
67bf50aa9a1df81dba235655
Song Mei
67bf50aa9a1df81dba235656
Jason E Weston
67bf50aa9a1df81dba235657
Weijie J. Su
67bf50aa9a1df81dba235658
Jing Xu
67bf50aa9a1df81dba235659
Linjun Zhang
An Overview of Large Language Models for Statisticians
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.
Larges Language Models can benefit from statistical methods to enhance trustworthiness, transparency, and address issues like uncertainty, interpretability, fairness, privacy, and adaptation.
Large Language Models LLMs uncertainty quantification interpretability fairness privacy watermarking model adaptation statistical analysis