ChatGPT, GPT-3.5, and other large language models (LLMs) have drawn
significant attention since their release, and the abilities of these models
have been investigated for a wide variety of tasks. In this research we
investigate to what extent GPT-3.5 can generate human-like comments on Dutch
news articles. We define human likeness as `not distinguishable from human
comments', approximated by the difficulty of automatic classification between
human and GPT comments. We analyze human likeness across multiple prompting
techniques. In particular, we utilize zero-shot, few-shot and context prompts,
for two generated personas. We found that our fine-tuned BERT models can easily
distinguish human-written comments from GPT-3.5 generated comments, with none
of the used prompting methods performing noticeably better. We further analyzed
that human comments consistently showed higher lexical diversity than
GPT-generated comments. This indicates that although generative LLMs can
generate fluent text, their capability to create human-like opinionated
comments is still limited.

研究了 GPT-3.5 在荷兰新闻文章上生成类似人类评论的能力，通过用多种提示技术进行人类相似度分析，并发现生成的 BERT 模型可以轻松区分人类写的评论和 GPT-3.5 生成的评论，且词汇多样性方面人类评论始终较高，表明生成性大语言模型在创造类似人类的有主观观点的评论方面仍然受限。