# Predicting the Quality of Revisions in Argumentative Writing

arXiv: https://arxiv.org/abs/2306.00667 · HTML: https://mlsanity.com/paper/2306.00667/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization](https://mlsanity.com/paper/2302.08081/) (2023)
- [Iteratively Prompt Pre-trained Language Models for Chain of Thought](https://mlsanity.com/paper/2203.08383/) (2022)
- [Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance](https://mlsanity.com/paper/1910.09180/) (2019)
- [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://mlsanity.com/paper/2101.10250/) (2021)
- [Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing](https://mlsanity.com/paper/2107.06990/) (2021)
- [ArgRewrite V.2: an Annotated Argumentative Revisions Corpus](https://mlsanity.com/paper/2206.01677/) (2022)
- [Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writing](https://mlsanity.com/paper/2302.05039/) (2023)
- [eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing](https://mlsanity.com/paper/1908.01992/) (2019)
- [Revision for Concision: A Constrained Paraphrase Generation Task](https://mlsanity.com/paper/2210.14257/) (2022)

## Built on by (most cited first, 7 of 7)

- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback](https://mlsanity.com/paper/2501.00715/) (2025)
- [A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality](https://mlsanity.com/paper/2404.02529/) (2024)
- [Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction](https://mlsanity.com/paper/2510.00268/) (2025)
- [CoCo Matrix: Taxonomy of Cognitive Contributions in Co-writing with Intelligent Agents](https://mlsanity.com/paper/2405.12438/) (2024)
- [Intention-Adaptive LLM Fine-Tuning for Text Revision Generation](https://mlsanity.com/paper/2602.00477/) (2026)
- [Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education](https://mlsanity.com/paper/2605.21614/) (2026)

## Abstract (arXiv)

The ability to revise in response to feedback is critical to students' writing success. In the case of argument writing in specific, identifying whether an argument revision (AR) is successful or not is a complex problem because AR quality is dependent on the overall content of an argument. For example, adding the same evidence sentence could strengthen or weaken existing claims in different argument contexts (ACs). To address this issue we developed Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality predictions. The experiments on two corpora, our annotated elementary essays and existing college essays benchmark, demonstrate the superiority of the proposed ACs over baselines.