Merge-of-experts (MoE) architectures use sparse activation to initialize scaling of model sizes while preserving high inference and training efficiency. However, ...
Quantification, an integral method of computational linguistics, is essential for managing the vast computational demands of implementing large language models ...
Understanding and mitigating hallucinations in vision and language models (VLVM) is an emerging field of research that addresses the generation ...
This paper has been accepted into the Data Issues for Foundation Models workshop at ICLR 2024. Large language models are ...
Introduction Visual language models (VLM) are revolutionizing the way machines understand and interact with both images and text. These models ...
We show that large language models (LLMs) can be tailored to be generalizable policies for embodied visual tasks. Our approach, ...
The rise of powerful Transformer-based language models (LMs) and their widespread use highlights the need to investigate their inner workings. ...
Large language models (LLMs) have gained ground for their exceptional performance on various tasks. Recent research aims to improve its ...
Instruction-based image editing improves the controllability and flexibility of image manipulation using natural commands without elaborate descriptions or regional masks. ...
Language models are incredibly powerful tools that can understand and generate human-like text by learning patterns from massive data sets. ...
