3 papers
cs.LG2026
UCS: Estimating Unseen Coverage for Improved In-Context Learning
Jiayi Xin, Xiang Li, Evan Qiang +4
In-context learning (ICL) performance depends critically on which demonstrations are placed in the prompt, yet most existing selectors prioritize heuristic notions of relevance or…
stat.ME2025
Do Large Language Models (Really) Need Statistical Foundations?
Weijie Su
Large language models (LLMs) represent a new paradigm for processing unstructured data, with applications across an unprecedented range of domains. In this paper, we address, throu…
stat.ML2025
An Overview of Large Language Models for Statisticians
Wenlong Ji, Weizhe Yuan, Emily Getzen +7
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation,…