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Advancing Student Writing Through Automated Syntax Feedback

Kamyar Zeinalipour, Mehak Mehak, Fatemeh Parsamotamed, Marco Maggini, Marco Gori

TL;DR

A specialized dataset named Essay-Syntax-Instruct is introduced designed to enhance the understanding and application of English syntax among students and demonstrates that the fine-tuned LLMs exhibit a marked improvement in addressing syntax-related challenges, thereby serving as a potent tool for students to identify and rectify their syntactic errors.

Abstract

This study underscores the pivotal role of syntax feedback in augmenting the syntactic proficiency of students. Recognizing the challenges faced by learners in mastering syntactic nuances, we introduce a specialized dataset named Essay-Syntax-Instruct designed to enhance the understanding and application of English syntax among these students. Leveraging the capabilities of Large Language Models (LLMs) such as GPT3.5-Turbo, Llama-2-7b-chat-hf, Llama-2-13b-chat-hf, and Mistral-7B-Instruct-v0.2, this work embarks on a comprehensive fine-tuning process tailored to the syntax improvement task. Through meticulous evaluation, we demonstrate that the fine-tuned LLMs exhibit a marked improvement in addressing syntax-related challenges, thereby serving as a potent tool for students to identify and rectify their syntactic errors. The findings not only highlight the effectiveness of the proposed dataset in elevating the performance of LLMs for syntax enhancement but also illuminate a promising path for utilizing advanced language models to support language acquisition efforts. This research contributes to the broader field of language learning technology by showcasing the potential of LLMs in facilitating the linguistic development of Students.

Advancing Student Writing Through Automated Syntax Feedback

TL;DR

A specialized dataset named Essay-Syntax-Instruct is introduced designed to enhance the understanding and application of English syntax among students and demonstrates that the fine-tuned LLMs exhibit a marked improvement in addressing syntax-related challenges, thereby serving as a potent tool for students to identify and rectify their syntactic errors.

Abstract

This study underscores the pivotal role of syntax feedback in augmenting the syntactic proficiency of students. Recognizing the challenges faced by learners in mastering syntactic nuances, we introduce a specialized dataset named Essay-Syntax-Instruct designed to enhance the understanding and application of English syntax among these students. Leveraging the capabilities of Large Language Models (LLMs) such as GPT3.5-Turbo, Llama-2-7b-chat-hf, Llama-2-13b-chat-hf, and Mistral-7B-Instruct-v0.2, this work embarks on a comprehensive fine-tuning process tailored to the syntax improvement task. Through meticulous evaluation, we demonstrate that the fine-tuned LLMs exhibit a marked improvement in addressing syntax-related challenges, thereby serving as a potent tool for students to identify and rectify their syntactic errors. The findings not only highlight the effectiveness of the proposed dataset in elevating the performance of LLMs for syntax enhancement but also illuminate a promising path for utilizing advanced language models to support language acquisition efforts. This research contributes to the broader field of language learning technology by showcasing the potential of LLMs in facilitating the linguistic development of Students.
Paper Structure (19 sections, 7 figures, 2 tables)

This paper contains 19 sections, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Automated Essay Scoring
  4. Essay Feedback Generation
  5. Methodology
  6. Essay-Syntax-Instruct Dataset
  7. Place Holder Replacement
  8. Syntax Feedback Generation
  9. Data Set Quality Evaluation
  10. From LLMs to Automatic Syntax Feedback Generation
  11. Experiments
  12. Experimental Setup
  13. Data.
  14. Baselines.
  15. Training details.
  16. ...and 4 more sections

Figures (7)

  • Figure 1: This figure illustrates the pipeline employed in this study: (a) Acquiring essays authored by humans from the ASAP dataset. (b) Data refinement and filtering to enhance data quality, by the essay placeholder replacement, eliminating excessively short or overly detailed essays. (c) Design of the prompt for generating syntax feedback based on input essay. (d) Exploit of GPT3.5-Turbo to generate syntax feedback from the collected data and defined prompts. (e) Fine-tune Large Language Models (LLMs) to enhance their ability to provide syntax feedback.
  • Figure 2: Place Holder Replacement Prompt.
  • Figure 3: Syntax Feedback Generation Prompt.
  • Figure 4: Token Distribution of essays and generated syntax feedback
  • Figure 5: Assessment results of human evaluations for the syntax feedback produced by GPT3.5-Turbo.
  • ...and 2 more figures