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.

翻译：近年来，大语言模型（LLM）的发展取得了快速进展。其中应用最广泛的模型之一是生成式预训练Transformer（GPT）系列，它已被应用于包括媒体领域在内的多个领域。然而，在实际应用中，媒体用例与通用LLM应用之间的差异日益凸显，尤其是在中文场景下。因此，开发专门满足媒体领域特殊需求的LLM变得愈发必要。本文提出MediaGPT，这是一个基于多样化媒体数据训练、面向中国媒体实际需求的大语言模型。我们设计了一系列多样化任务指令类型，以满足领域的特定需求。为验证所提LLM的有效性，我们构建了面向媒体领域的独特数据集，并开发了专门针对生成式任务设计的验证方法。通过上述工作，我们旨在弥合通用LLM与媒体领域需求之间的差距，为该领域更高效、更有效地应用LLM铺平道路。本文旨在探索开发面向媒体应用的LLM所面临的挑战与机遇，并提出应对这些挑战的潜在解决方案。