3 papers
cs.CL2026
D-Models and E-Models: Diversity-Stability Trade-offs in the Sampling Behavior of Large Language Models
Jia Gu, Liang Pang, Huawei Shen +1
The predictive probability of the next token (P_token) in large language models (LLMs) is inextricably linked to the probability of relevance for the next piece of information, the…
cs.CL2025
Large Language Model Sourcing: A Survey
Liang Pang, Jia Gu, Sunhao Dai +7
Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement…
cs.CL2025
RLKD: Distilling LLMs' Reasoning via Reinforcement Learning
Shicheng Xu, Liang Pang, Yunchang Zhu +6
Distilling reasoning paths from teacher to student models via supervised fine-tuning (SFT) provides a shortcut for improving the reasoning ability of smaller Large Language Models…