GPT-4 Technical Report
| dc.contributor.author | OpenAI | |
| dc.date.accessioned | 2025-06-02T13:22:57Z | |
| dc.date.available | 2025-06-02T13:22:57Z | |
| dc.date.issued | 2023-03-15 | |
| dc.description | GPT-4는 멀티모달 입력을 수용하는 고성능 LLM으로, 벤치마크 평가에서 GPT-3.5 대비 향상된 언어 이해 및 생성 능력을 보입니다. ©2023 OpenAI This work was authored by the OpenAI research team with contributions from 71 individuals. Due to the extensive author list, only the organizational author is listed in the metadata. The full list of authors is provided below for reference: https://arxiv.org/abs/2303.08774 | |
| dc.description.abstract | 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. | |
| dc.description.sponsorship | OpenAI | |
| dc.identifier.uri | https://arxiv.org/abs/2303.08774 | |
| dc.identifier.uri | http://data.inu.ac.kr/handle/123456789/1957 | |
| dc.language.iso | en_US | |
| dc.publisher | arXiv | |
| dc.subject | GPT-4 | |
| dc.subject | LLM | |
| dc.subject | Multimodal | |
| dc.subject | Alignment | |
| dc.subject | Transformer | |
| dc.title | GPT-4 Technical Report | |
| dc.type | Article |