# An Overview of Large Language Models for Statisticians

arXiv: https://arxiv.org/abs/2502.17814 · HTML: https://mlsanity.com/paper/2502.17814/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2025.

## Built on by (most cited first, 24 of 24)

- [Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium](https://mlsanity.com/paper/2503.10990/) (2025)
- [AdaDetectGPT: Adaptive Detection of LLM-Generated Text with Statistical Guarantees](https://mlsanity.com/paper/2510.01268/) (2025)
- [An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainability](https://mlsanity.com/paper/2504.04462/) (2025)
- [Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching](https://mlsanity.com/paper/2505.20627/) (2025)
- [Uncertainty Quantification for Large Language Model Reward Learning under Heterogeneous Human Feedback](https://mlsanity.com/paper/2512.03208/) (2025)
- [Learning Rate Scaling across LoRA Ranks and Transfer to Full Finetuning](https://mlsanity.com/paper/2602.06204/) (2026)
- [Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://mlsanity.com/paper/2510.18333/) (2025)
- [Do Large Language Models (Really) Need Statistical Foundations?](https://mlsanity.com/paper/2505.19145/) (2025)
- [A Collectivist, Economic Perspective on AI](https://mlsanity.com/paper/2507.06268/) (2025)
- [Statistical Methods in Generative AI](https://mlsanity.com/paper/2509.07054/) (2025)
- [Human-Inspired Learning for Large Language Models via Obvious Record and Maximum-Entropy Method Discovery](https://mlsanity.com/paper/2512.12608/) (2025)
- [Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation](https://mlsanity.com/paper/2604.19444/) (2026)
- [Accelerating RLHF Training with Reward Variance Increase](https://mlsanity.com/paper/2505.23247/) (2025)
- [Refined Detection for Gumbel Watermarking](https://mlsanity.com/paper/2603.30017/) (2026)
- [Sequential statistical inference for Large Language Models: Representation, validity, and monitoring](https://mlsanity.com/paper/2606.07624/) (2026)
- [VESTA: Visual Exploration with Statistical Tool Agents](https://mlsanity.com/paper/2606.00384/) (2026)
- [α-LoRA: Effective Fine-Tuning via Base Model Rescaling](https://mlsanity.com/paper/2510.21345/) (2025)
- [MALBO: Optimizing LLM-Based Multi-Agent Teams via Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2511.11788/) (2025)
- [Variance-aware Reward Modeling with Anchor Guidance](https://mlsanity.com/paper/2605.11865/) (2026)
- [READER: Reasoning-Enhanced AI-Generated Text Detection](https://mlsanity.com/paper/2605.25281/) (2026)
- [StatABench: Dataset and Framework for Evaluating Statistical Analysis Capabilities of LLMs](https://mlsanity.com/paper/2606.22977/) (2026)
- [Grading the Grader: Lessons from Evaluating an Agentic Data Analysis System](https://mlsanity.com/paper/2606.24839/) (2026)
- [Minimax bounds for watermarked and masked recursive discrete distribution estimation](https://mlsanity.com/paper/2608.31091/) (2026)
- [Augmented Hypothesis Testing with Persona-Based LLM Simulations](https://mlsanity.com/paper/2609.24629/) (2026)

## Abstract (arXiv)

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.