An Overview of Large Language Models for Statisticians
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910843296284672 |
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| author | Ji, Wenlong Yuan, Weizhe Getzen, Emily Cho, Kyunghyun Jordan, Michael I. Mei, Song Weston, Jason E Su, Weijie J. Xu, Jing Zhang, Linjun |
| author_facet | Ji, Wenlong Yuan, Weizhe Getzen, Emily Cho, Kyunghyun Jordan, Michael I. Mei, Song Weston, Jason E Su, Weijie J. Xu, Jing Zhang, Linjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17814 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Overview of Large Language Models for Statisticians Ji, Wenlong Yuan, Weizhe Getzen, Emily Cho, Kyunghyun Jordan, Michael I. Mei, Song Weston, Jason E Su, Weijie J. Xu, Jing Zhang, Linjun Machine Learning Artificial Intelligence Computation and Language 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. |
| title | An Overview of Large Language Models for Statisticians |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2502.17814 |