AI-Generated News Articles Based on Large Language Models
Kai Jiang, Qilai Zhang, Dongsheng Guo, Dengrong Huang, Sijia Zhang, Zizhong Wei, Fanggang Ning, Rui Li · 2023
News generation, as a subset of long-form text generation task, is an important challenge in natural language processing. The creation of news articles necessitates a foundation in factual events, demanding the authenticity of content. The current large language models (LLMs) are capable of generating fluent text. However, these models often result in the generation of text that includes unfounded or inaccurate information, a phenomenon known as hallucinations. To ease this issue, we introduce a novel method. This approach leverages structured prompt-template inputs to guide the generation process, coupled with a post-checking module to ensure content authenticity. Furthermore, we propose two novel metrics, namely, topic consistency and core consistency, to evaluate the quality of the generated news. We have conducted extensive experiments using real news datasets, and our findings indicate that the proposed framework demonstrably mitigates the incidence of hallucinations in algorithmically generated news content, concurrently enhancing the overall quality of the produced news articles.