Journalist Assistant: An Innovative Solution for Efficient News Reporting Generation

With the development of large language models (LLMs), their capabilities in understanding and generating high-quality text have significantly improved. However, due to the lack of specialized optimization for the field of news reporting, existing LLMs exhibit certain limitations when handling news report generation tasks. These limitations include, but are not limited to, unreasonable text structure, non-standard language expression, and a lack of depth in content. Such issues not only restrict the application scope of LLMs in the news domain but may also impact the quality of news reports and the reading experience of audiences. To address these challenges, this study proposes the Journalist Assistant system, a high-quality LLM instance specifically designed for news report generation. By leveraging a large-scale pre-trained model based on Qwen, the Journalist Assistant system achieves commendable performance in news report generation tasks. Experimental results show that compared to other open-source LLMs, the Journalist Assistant system demonstrates outstanding performance in news report generation tasks, particularly in terms of accuracy, linguistic coherence, and content depth, reaching industry-leading standards. This achievement not only provides new possibilities for the practical application of LLMs in the news domain but also lays a solid foundation for the future development of more intelligent and efficient news assistance tools.