%0 Journal Article %1 OpenAI2023GPT4TR %A OpenAI, %D 2023 %J ArXiv %K GPT-4 OpenAI Technical_Report ArXiv Machine_Learning posted_with_chatgpt %T GPT-4 Technical Report %V abs/2303.08774

@article{OpenAI2023GPT4TR, added-at = {2023-07-26T15:30:50.000+0200}, author = {OpenAI}, biburl = {https://www.bibsonomy.org/bibtex/2b87062f1a9478148d2e5dd0006c9c455/dblp_test}, description = {We report the development of GPT-4, 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 Transformer-based 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.}, interhash = {241e35649065841f159e6105eb87b1d3}, intrahash = {b87062f1a9478148d2e5dd0006c9c455}, journal = {ArXiv}, keywords = {GPT-4 OpenAI Technical_Report ArXiv Machine_Learning posted_with_chatgpt}, timestamp = {2024-04-29T15:49:15.000+0200}, title = {GPT-4 Technical Report}, volume = {abs/2303.08774}, year = 2023 }