6 papers
Merging Beyond: Streaming LLM Updates via Activation-Guided Rotations
Yuxuan Yao, Haonan Sheng, Qingsong Lv +11
The escalating scale of Large Language Models (LLMs) necessitates efficient adaptation techniques. Model merging has gained prominence for its efficiency and controllability. Howev…
Do Language Model Agents Align with Humans in Rating Visualizations? An Empirical Study
Zekai Shao, Yi Shan, Yixuan He +6
Large language models encode knowledge in various domains and demonstrate the ability to understand visualizations. They may also capture visualization design knowledge and potenti…
Activation-Guided Consensus Merging for Large Language Models
Yuxuan Yao, Shuqi Liu, Zehua Liu +6
Recent research has increasingly focused on reconciling the reasoning capabilities of System 2 with the efficiency of System 1. While existing training-based and prompt-based appro…
Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging
Han Wu, Yuxuan Yao, Shuqi Liu +7
The transition from System 1 to System 2 reasoning in large language models (LLMs) has marked significant advancements in handling complex tasks through deliberate, iterative think…
From System 1 to System 2: A Survey of Reasoning Large Language Models
Zhong-Zhi Li, Duzhen Zhang, Ming-Liang Zhang +18
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in qu…
LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging
Zehua Liu, Han Wu, Yuxuan Yao +4
While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its abil…