Generating natural and meaningful responses to communicate with multi-modal human inputs is a fundamental capability of Large Vision-Language Models (L VLMs).

Y ou are an AI visual assistant, and you are seeing a single image.

Parallelism introduces collective communication that is both expensive and represents a phase when hardware resources are underuti-lized.

Thousands of new scientific papers are published each month.

In this work, we focus on mathematical problem-solving to explore enhancement of the mathematical reasoning abilities of pretrained LLMs. We investigate instruction tuning (Longpre et al., 2023; Wang

Large Language Models (LLMs) are distinguished by their massive parameter counts, which typically result in significant redundancy.