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Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency

2023-02-12 · Oğuz 'Oz' Buruk

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.

📄 PDF Abstract BibTeX arXiv:2304.11079

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Multi-Head Attention 설명 없음
Attention 설명 없음
Cosine Annealing Cosine Annealing is a type of learning rate schedule that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Adam 설명 없음
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

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