37 citations · 67 across the 7 of their papers we have counts for
4 papers · 1 filter
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Hongyu Wang, Shuming Ma, Ruiping Wang +1
We introduce, Q-Sparse, a simple yet effective approach to training sparsely-activated large language models (LLMs). Q-Sparse enables full sparsity of activations in LLMs which can…
AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation
Chuyan Xiong, Chengyu Shen, Xiaoqi Li +4
The ability to reflect on and correct failures is crucial for robotic systems to interact stably with real-life objects.Observing the generalization and reasoning capabilities of M…
M4U: Evaluating Multilingual Understanding and Reasoning for Large Multimodal Models
Hongyu Wang, Jiayu Xu, Senwei Xie +6
Multilingual capability is an essential aspect for large multimodal models, since they are usually deployed across various countries and languages. However, most existing benchmark…
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Shuming Ma, Hongyu Wang, Lingxiao Ma +7
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in whic…