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cs.CL2025
AdaptThink: Reasoning Models Can Learn When to Think
Jiajie Zhang, Nianyi Lin, Lei Hou +2
Recently, large reasoning models have achieved impressive performance on various tasks by employing human-like deep thinking. However, the lengthy thinking process substantially in…
cs.CL2024★ 1 cited
Reverse That Number! Decoding Order Matters in Arithmetic Learning
Daniel Zhang-Li, Nianyi Lin, Jifan Yu +6
Recent advancements in pretraining have demonstrated that modern Large Language Models (LLMs) possess the capability to effectively learn arithmetic operations. However, despite ac…
cs.CL2023
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation
Jing Zhang, Xiaokang Zhang, Daniel Zhang-Li +10
We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet kno…