Abstract
In recent years, the rapid development of large-scale language models has attracted much attention in natural language processing. This paper focuses on large-scale models such as ChatGPT and provides insights into the advancement and application of key technologies used in these models. By exploring model architectures, pre-training techniques, transfer learning, self-supervised learning, multimodal learning, fine-grained control, and long-text processing, we reveal how these techniques have driven the evolution of language models, leading to notable achievements in various fields. By providing insights into these technologies, we aim to provide researchers and practitioners with a comprehensive perspective on the challenges and opportunities in language processing.
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