title: An Overview of Large Language Models for Statisticians
summary: 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.
paper_authors._id: 67bf50aa9a1df81dba235650
paper_authors.name: Wenlong Ji
paper_authors._id: 67bf50aa9a1df81dba235651
paper_authors.name: Weizhe Yuan
paper_authors._id: 67bf50aa9a1df81dba235652
paper_authors.name: Emily Getzen
paper_authors._id: 67bf50aa9a1df81dba235653
paper_authors.name: Kyunghyun Cho
paper_authors._id: 67bf50aa9a1df81dba235654
paper_authors.name: Michael I. Jordan
paper_authors._id: 67bf50aa9a1df81dba235655
paper_authors.name: Song Mei
paper_authors._id: 67bf50aa9a1df81dba235656
paper_authors.name: Jason E Weston
paper_authors._id: 67bf50aa9a1df81dba235657
paper_authors.name: Weijie J. Su
paper_authors._id: 67bf50aa9a1df81dba235658
paper_authors.name: Jing Xu
paper_authors._id: 67bf50aa9a1df81dba235659
paper_authors.name: Linjun Zhang
paper_title: An Overview of Large Language Models for Statisticians
paper_summary: 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.