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

• URL: http://arxiv.org/abs/2412.14986v1
• Date: Thu, 19 Dec 2024 15:58:53 GMT
• Title: Chain-of-MetaWriting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts
• Authors: Ioana Buhnila, Georgeta Cislaru, Amalia Todirascu,
• Abstract summary: This paper introduces a fine-grained linguistic and textual analysis of multilingual Small Language Models' (SLMs) writing. We mainly focused on short story and essay writing tasks in French for schoolchildren and undergraduate students respectively.
• Score: 0.8192907805418583
• License:
• Abstract: 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.

Related papers

• Human Variability vs. Machine Consistency: A Linguistic Analysis of Texts Generated by Humans and Large Language Models [0.0]
  We identify significant differences between human-written texts and those generated by large language models (LLMs) Our findings indicate that humans write texts that are less cognitively demanding, with higher semantic content, and richer emotional content compared to texts generated by LLMs.
  arXiv  Detail & Related papers  (2024-12-04T04:38:35Z)
• Visually Grounded Language Learning: a review of language games, datasets, tasks, and models [60.2604624857992]
  Many Vision+Language (V+L) tasks have been defined with the aim of creating models that can ground symbols in the visual modality. In this work, we provide a systematic literature review of several tasks and models proposed in the V+L field.
  arXiv  Detail & Related papers  (2023-12-05T02:17:29Z)
• Toward Joint Language Modeling for Speech Units and Text [89.32163954508489]
  We explore joint language modeling for speech units and text. We introduce automatic metrics to evaluate how well the joint LM mixes speech and text. Our results show that by mixing speech units and text with our proposed mixing techniques, the joint LM improves over a speech-only baseline on SLU tasks.
  arXiv  Detail & Related papers  (2023-10-12T20:53:39Z)
• Teach LLMs to Personalize -- An Approach inspired by Writing Education [37.198598706659524]
  We propose a general approach for personalized text generation using large language models (LLMs) Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation.
  arXiv  Detail & Related papers  (2023-08-15T18:06:23Z)
• TinyStories: How Small Can Language Models Be and Still Speak Coherent English? [37.65216279977461]
  Language models (LMs) often struggle to produce coherent and fluent text when they are small. We introduce TinyStories, a dataset of short stories that only contain words that a typical 3 to 4-year-old usually understand. We show that TinyStories can be used to train and evaluate LMs that are much smaller than the state-of-the-art models.
  arXiv  Detail & Related papers  (2023-05-12T20:56:48Z)
• MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective [22.69509556890676]
  We propose a novel multi-task training strategy for coherent text generation grounded on the cognitive theory of writing. We extensively evaluate our model on three open-ended generation tasks including story generation, news article writing and argument generation.
  arXiv  Detail & Related papers  (2022-10-26T11:55:41Z)
• SCROLLS: Standardized CompaRison Over Long Language Sequences [62.574959194373264]
  We introduce SCROLLS, a suite of tasks that require reasoning over long texts. SCROLLS contains summarization, question answering, and natural language inference tasks. We make all datasets available in a unified text-to-text format and host a live leaderboard to facilitate research on model architecture and pretraining methods.
  arXiv  Detail & Related papers  (2022-01-10T18:47:15Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
  This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
  arXiv  Detail & Related papers  (2020-10-14T17:54:42Z)
• Enabling Language Models to Fill in the Blanks [81.59381915581892]
  We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document. We train (or fine-tune) off-the-shelf language models on sequences containing the concatenation of artificially-masked text and the text which was masked. We show that this approach, which we call infilling by language modeling, can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics.
  arXiv  Detail & Related papers  (2020-05-11T18:00:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.