The development of large language models (LLMs) has seen rapid progress in
recent years. One of the most widely used LLMs is the Generative Pre-trained
Transformer (GPT) series, which has been applied in various fields, including
the media domain. However, in practical applications, the differences between
the media's use cases and the general-purpose applications of LLMs have become
increasingly apparent, especially Chinese. As a result, there is a growing need
to develop LLM that are specifically tailored to the unique requirements of the
media domain. In this paper, we present MediaGPT, a large language model
training on variety of media data and addressing the practical needs of Chinese
media. We have designed a diverse set of task instruction types to cater to the
specific requirements of the domain. To further validate the effectiveness of
our proposed LLM, we have constructed unique datasets that are tailored to the
media domain and have also developed verification methods that are specifically
designed for generative-type tasks. By doing so, we aim to bridge the gap
between the general-purpose LLM and the requirements of the media domain, and
to pave the way for more effective and efficient use of LLM in this field. This
paper aims to explore the challenges and opportunities of developing LLM for
media applications and to propose potential solutions for addressing these
challenges.

本文旨在研究为媒体应用开发大型语言模型（LLMs）的挑战和机会，并提出解决这些挑战的潜在方案。