OpenAI released GPT-4. It is a multimodel that handles video, images and sound along with text.
GPT 4 Technical report is here.
GPT-4 is a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformerbased model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4’s performance based on models trained with no more than 1/1,000th the compute of GPT-4.
On a simulated bar exam, GPT-4 achieves a score that falls in the top 10% of test takers.
This contrasts with GPT-3.5, which scores in the bottom 10%.
On a suite of traditional NLP benchmarks, GPT-4 outperforms both previous large language models
and most state-of-the-art systems (which often have benchmark-specific training or hand-engineering).
On the MMLU benchmark, an English-language suite of multiple-choice questions covering 57 subjects, GPT-4 not only outperforms existing models by a considerable margin in English, but also demonstrates strong performance in other languages. On translated variants of MMLU, GPT-4 surpasses the English-language state-of-the-art in 24 of 26 languages considered. GPT4 model capability results, as well as model safety improvements and results, in more detail in later sections in the GPT 4 Technical report.>
Announcing GPT-4, a large multimodal model, with our best-ever results on capabilities and alignment: https://t.co/TwLFssyALF pic.twitter.com/lYWwPjZbSg
— OpenAI (@OpenAI) March 14, 2023
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