# Chain-of-MetaWriting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts

arXiv: https://arxiv.org/abs/2412.14986 · HTML: https://mlsanity.com/paper/2412.14986/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [FineSurE: Fine-grained Summarization Evaluation using LLMs](https://mlsanity.com/paper/2407.00908/) (2024)
- [On Learning to Summarize with Large Language Models as References](https://mlsanity.com/paper/2305.14239/) (2023)
- [Are Large Language Models Capable of Generating Human-Level Narratives?](https://mlsanity.com/paper/2407.13248/) (2024)
- [Applying Large Language Models and Chain-of-Thought for Automatic Scoring](https://mlsanity.com/paper/2312.03748/) (2023)
- [Multiscale Positive-Unlabeled Detection of AI-Generated Texts](https://mlsanity.com/paper/2305.18149/) (2023)
- [Stylometric Detection of AI-Generated Text in Twitter Timelines](https://mlsanity.com/paper/2303.03697/) (2023)
- [CroissantLLM: A Truly Bilingual French-English Language Model](https://mlsanity.com/paper/2402.00786/) (2024)
- [LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback](https://mlsanity.com/paper/2406.01771/) (2024)
- [Experimental Narratives: A Comparison of Human Crowdsourced Storytelling and AI Storytelling](https://mlsanity.com/paper/2310.12902/) (2023)
- [Small Language Models are Good Too: An Empirical Study of Zero-Shot Classification](https://mlsanity.com/paper/2404.11122/) (2024)

## Built on by (most cited first, 2 of 2)

- [Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language](https://mlsanity.com/paper/2503.01539/) (2025)
- [CLEAR: A Comprehensive Linguistic Evaluation of Argument Rewriting by Large Language Models](https://mlsanity.com/paper/2509.15027/) (2025)

## Abstract (arXiv)

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.