小模型模仿学生写作过程，暴露了对儿童语境的适应短板。
Chain-of-MetaWriting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts
- 用链式元写作框架让小模型模拟人类写作规划与评估步骤。
- 在法语小学生作文中，模型对校园暴力等敏感话题处理不当，用词过难。
- 生成文本在时间衔接、主题推进和指代关系上明显不如真人作品连贯。
大型语言模型虽能生成报告、议论文和故事等文本，但缺乏对写作过程的元认知能力，也无类比于年幼人类学习者的沟通需求。本文提出一种细粒度的语言学与文本分析方法——链式元写作（Chain-of-MetaWriting），用于研究多语言小型语言模型（SLMs）的写作行为。重点考察法语环境下小学生短篇故事和本科生议论文任务。结果显示，当面对校园暴力等敏感议题时，模型难以适配儿童读者，常使用超出目标受众理解水平的词汇。尤其在时间连接词、主题演进和指代一致性方面，生成文本与人类作品相比，在语篇连贯性和衔接性上存在显著差距。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have been used to generate texts in response to different writing tasks: reports, essays, story telling. However, language models do not have a meta-representation of the text writing process, nor inherent communication learning needs, comparable to those of young human students. This paper introduces a fine-grained linguistic and textual analysis of multilingual Small Language Models' (SLMs) writing. With our method, Chain-of-MetaWriting, SLMs can imitate some steps of the human writing process, such as planning and evaluation. We mainly focused on short story and essay writing tasks in French for schoolchildren and undergraduate students respectively. Our results show that SLMs encounter difficulties in assisting young students on sensitive topics such as violence in the schoolyard, and they sometimes use words too complex for the target audience. In particular, the output is quite different from the human produced texts in term of text cohesion and coherence regarding temporal connectors, topic progression, reference.
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