Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency
Not on the map. Why?
Abstract (arXiv)
The debate around the use of GPT 3.5 has been a popular topic among academics since the release of ChatGPT. Whilst some have argued for the advantages of GPT 3.5 in enhancing academic writing, others have raised concerns such as plagiarism, the spread of false information, and ecological issues. The need for finding ways to use GPT 3.5 models transparently has been voiced, and suggestions have been made on social media as to how to use GPT 3.5 models in a smart way. Nevertheless, to date, there is a lack of literature which clearly outlines how to use GPT 3.5 models in academic writing, how effective they are, and how to use them transparently. To address this, I conducted a personal experience experiment with GPT 3.5, specifically by using OpenAI text davinci 003 model, for writing this article. I identified five ways of using GPT 3.5: Chunk Stylist, Bullet to Paragraph, Talk Textualizer, Research Buddy, and Polisher. I reflected on their efficacy, and commented on their potential impact on writing ethics. Additionally, I provided a comprehensive document which shows the prompts I used, results I got from GPT 3.5, the final edits and visually compares those by showing the differences in percentages.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 12 Feb 2023.
Builds on
3 of 19 references.
References · 3
- 2020The Radicalization Risks of GPT-3 and Advanced Neural Language Modelscited by 76
- 2021Detecting Hate Speech with GPT-3cited by 55
- 2022How Large Language Models are Transforming Machine-Paraphrased Plagiarismcited by 38
Built on by
Citing papers per year: 2023: 3; 2024: 3.
6 papers.
- 2024When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settingscited by 17
- 2023Friend or Foe? Exploring the Implications of Large Language Models on the Science Systemcited by 15
- 2024Automating psychological hypothesis generation with AI: when large language models meet causal graphcited by 15
- 2024Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Compositioncited by 12
- 2023Generative AI: Implications and Applications for Educationcited by 8
- 2023System-Level Natural Language Feedbackcited by 4
- 2024When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings5 Jul 2024
- 2024Automating psychological hypothesis generation with AI: when large language models meet causal graph22 Feb 2024
- 2024Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition19 Feb 2024
- 2023System-Level Natural Language Feedback23 Jun 2023
- 2023Friend or Foe? Exploring the Implications of Large Language Models on the Science System16 Jun 2023
- 2023Generative AI: Implications and Applications for Education12 May 2023