118 citations · 213 across the 5 of their papers we have counts for
5 papers · 1 filter
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
Shang Liu, Zhongze Cai, Guanting Chen +1
Predicting simple function classes has been widely used as a testbed for developing theory and understanding of the trained Transformer's in-context learning (ICL) ability. In this…
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making
Hanzhao Wang, Yu Pan, Fupeng Sun +4
In this paper, we consider the supervised pre-trained transformer for a class of sequential decision-making problems. The class of considered problems is a subset of the general fo…
Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
Linyu Liu, Yu Pan, Xiaocheng Li +1
In this paper, we study the problem of uncertainty estimation and calibration for LLMs. We begin by formulating the uncertainty estimation problem, a relevant yet underexplored are…
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
Daya Guo, Qihao Zhu, Dejian Yang +10
The rapid development of large language models has revolutionized code intelligence in software development. However, the predominance of closed-source models has restricted extens…
DeepSeek LLM: Scaling Open-Source Language Models with Longtermism
DeepSeek-AI, :, Xiao Bi +85
The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions,…