  Recently, the intersection of Large Language Models (LLMs) and Computer
Vision (CV) has emerged as a pivotal area of research, driving significant
advancements in the field of Artificial Intelligence (AI). As transformers have
become the backbone of many state-of-the-art models in both Natural Language
Processing (NLP) and CV, understanding their evolution and potential
enhancements is crucial. This survey paper delves into the latest progressions
in the domain of transformers and their subsequent successors, emphasizing
their potential to revolutionize Vision Transformers (ViTs) and LLMs. This
survey also presents a comparative analysis, juxtaposing the performance
metrics of several leading paid and open-source LLMs, shedding light on their
strengths and areas of improvement as well as a literature review on how LLMs
are being used to tackle vision related tasks. Furthermore, the survey presents
a comprehensive collection of datasets employed to train LLMs, offering
insights into the diverse data available to achieve high performance in various
pre-training and downstream tasks of LLMs. The survey is concluded by
highlighting open directions in the field, suggesting potential venues for
future research and development. This survey aims to underscores the profound
intersection of LLMs on CV, leading to a new era of integrated and advanced AI
models.
