May 15, 2023, 12:46 a.m. | Zhengqing Yuan, Huiwen Xue, Xinyi Wang, Yongming Liu, Zhuanzhe Zhao, Kun Wang

cs.CL updates on arXiv.org arxiv.org

In recent years, large language models (LLMs) have made significant progress
in natural language processing (NLP), with models like ChatGPT and GPT-4
achieving impressive capabilities in various linguistic tasks. However,
training models on such a large scale is challenging, and finding datasets that
match the model's scale is often difficult. Fine-tuning and training models
with fewer parameters using novel methods have emerged as promising approaches
to overcome these challenges. One such model is MiniGPT-4, which achieves
comparable vision-language understanding to …

arxiv chatgpt datasets fine-tuning gpt gpt-4 language language models language processing language understanding large language models llms minigpt minigpt-4 natural natural language natural language processing nlp processing progress scale training understanding vision

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