2 citations · 2 across the 4 of their papers we have counts for
4 papers
Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs
Yuqi Zhu, Ge Li, Xue Jiang +4
Chain-of-Thought (CoT) reasoning has been demonstrated as an effective technique for improving the problem-solving capabilities of large language models (LLMs) in the context of co…
Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling
Yihong Dong, Ge Li, Xue Jiang +8
Periodicity, as one of the most important basic characteristics, lays the foundation for facilitating structured knowledge acquisition and systematic cognitive processes within hum…
Theoretical Proof that Auto-regressive Language Models Collapse when Real-world Data is a Finite Set
Lecheng Wang, Xianjie Shi, Ge Li +5
Auto-regressive language models (LMs) have been widely used to generate data in data-scarce domains to train new LMs, compensating for the scarcity of real-world data. Previous wor…
Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models
Yuqi Zhu, Jia Li, Ge Li +3
Recently, Large Language Models (LLMs) have shown impressive abilities in code generation. However, existing LLMs' decoding strategies are designed for Natural Language (NL) genera…