5 papers
GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity
Shuang Liang, Xin-Yu Hu, Xiang-Jun Ou +1
Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However, the reasoning processes of LLMs often exhibit uncertainty, where LLMs…
The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models
Xiang-Jun Ou, Shuang Liang, Xin-Yu Hu +3
Recent advancements in Large Language Models (LLMs) have enabled sophisticated reasoning and content generation, yet their inherent stochasticity poses significant challenges for e…
On the Expressive Power of Weight Quantization in Large Language Models
Shao-Qun Zhang
In recent years, weight quantization that encodes the learnable parameters of large language models in an -bit format has garnered significant attention due to its potential for…
TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge
Shu-Hao Zhang, Wei-Cheng Tang, Chen Wu +5
Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, w…
Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance
Jiayi Chen, Chen Wu, Shaoqun Zhang +3
Embedding models have become essential tools in both natural language processing and computer vision, enabling efficient semantic search, recommendation, clustering, and more. Howe…