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 eleven 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. The evaluation framework and experimental results are expected to provide an in-depth understanding of the editorial capabilities of LLMs and speed up the development of LLMs in journalism.

We explore the relationship between empathy and toxicity in the context of online mental health forums. Despite the common assumption of a negative correlation between these concepts, it has not been empirically examined. We augment the EPITOME mental health empathy dataset with toxicity labels using two widely employed toxic/harmful content detection APIs: Perspective API and OpenAI moderation API. We find a notable presence of toxic/harmful content (17.77%) within empathetic responses, and only a very weak negative correlation between the two variables. Qualitative analysis revealed contributions labeled as empathetic often contain harmful content such as promotion of suicidal ideas. Our results highlight the need for reevaluating empathy independently from toxicity in future research and encourage a reconsideration of empathy’s role in natural language generation and evaluation.