5 papers
Evaluating Memory Capability in Continuous Lifelog Scenario
Jianjie Zheng, Zhichen Liu, Zhanyu Shen +6
Nowadays, wearable devices can continuously lifelog ambient conversations, creating substantial opportunities for memory systems. However, existing benchmarks primarily focus on on…
Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilities
Zhichen Liu, Yongyuan Li, Yang Xu
Researchers have explored different ways to improve large language models (LLMs)' capabilities via dummy token insertion in contexts. However, existing works focus solely on the du…
Fast and Accurate Probing of In-Training LLMs' Downstream Performances
Zhichen Liu, Tianle Lun, Zhibin Wen +7
The paradigm of scaling Large Language Models (LLMs) in both parameter size and test time has pushed the boundaries of AI capabilities, but at the cost of making the traditional ge…
ROOT: Robust Orthogonalized Optimizer for Neural Network Training
Wei He, Kai Han, Hang Zhou +4
The optimization of large language models (LLMs) remains a critical challenge, particularly as model scaling exacerbates sensitivity to algorithmic imprecision and training instabi…
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
Yehui Tang, Xiaosong Li, Fangcheng Liu +19
The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because on…