2 citations · 2 across the 4 of their papers we have counts for
4 papers
Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs
Tanzila Rahman, Mehran Taghian Jazi, Yunke Peng +10
Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent hardware support for low-precis…
HiFloat4 Format for Language Model Pre-training on Ascend NPUs
Mehran Taghian, Yunke Peng, Xing Huang +22
Training large foundation models at low numerical precision is one of the most promising directions for reducing the compute and memory cost of modern AI. Recent 4-bit floating-poi…
Exploring the Heterogeneity of Tabular Data: A Diversity-aware Data Generator via LLMs
Yafeng Tang, Xiaoou Ding, Jianzhuo Du +5
Tabular data generation has become increasingly essential for enabling robust machine learning applications, which require large-scale, high-quality data. Existing solutions levera…
How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games
Yutong Xie, Yiyao Liu, Zhuang Ma +5
The deployment of large language models (LLMs) in diverse applications requires a thorough understanding of their decision-making strategies and behavioral patterns. As a supplemen…