NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism
<|reference_start|>NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism: We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focused on four facets of writing proficiency and six facets of safety adherence, and it comprises manually and carefully designed 1,267 test samples in the types of multiple choice questions and short answer questions for five editorial tasks in 24 news domains. To measure performances, we propose different GPT-4 based automatic evaluation protocols to assess LLM generations for short answer questions in terms of writing proficiency and safety adherence, and both are validated by the high correlations with human evaluations. Based on the systematic evaluation framework, we conduct a comprehensive analysis of ten popular LLMs which can handle Chinese. The experimental results highlight GPT-4 and ERNIE Bot as top performers, yet reveal a relative deficiency in journalistic safety adherence in creative writing tasks. Our findings also underscore the need for enhanced ethical guidance in machine-generated journalistic content, marking a step forward in aligning LLMs with journalistic standards and safety considerations.<|reference_end|>
@article{li2024newsbench:,
    title={NewsBench: A Systematic Evaluation Framework for Assessing Editorial
  Capabilities of Large Language Models in Chinese Journalism},
    author={Miao Li and Ming-Bin Chen and Bo Tang and Shengbin Hou and Pengyu Wang
  and Haiying Deng and Zhiyu Li and Feiyu Xiong and Keming Mao and Peng Cheng
  and Yi Luo},
    journal={arXiv preprint arXiv:2403.00862},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00862},
    primaryClass={cs.CL cs.AI}
}