# Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizability

arXiv: https://arxiv.org/abs/2308.01391 · HTML: https://mlsanity.com/paper/2308.01391/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Adaptive Machine Translation with Large Language Models](https://mlsanity.com/paper/2301.13294/) (2023)
- [How to Design Translation Prompts for ChatGPT: An Empirical Study](https://mlsanity.com/paper/2304.02182/) (2023)
- [Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses](https://mlsanity.com/paper/2303.15587/) (2023)

## Built on by (most cited first, 11 of 11)

- [A Prompt Response to the Demand for Automatic Gender-Neutral Translation](https://mlsanity.com/paper/2402.06041/) (2024)
- [Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts](https://mlsanity.com/paper/2403.00127/) (2024)
- [Specification-Aware Machine Translation and Evaluation for Purpose Alignment](https://mlsanity.com/paper/2509.17559/) (2025)
- [Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations](https://mlsanity.com/paper/2605.13596/) (2026)
- [Double Jeopardy and Climate Impact in the Use of Large Language Models: Socio-economic Disparities and Reduced Utility for Non-English Speakers](https://mlsanity.com/paper/2410.10665/) (2024)
- [Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent](https://mlsanity.com/paper/2606.03259/) (2026)
- [Two Timin': Repairing Smart Contracts With A Two-Layered Approach](https://mlsanity.com/paper/2309.07841/) (2023)
- [Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models](https://mlsanity.com/paper/2403.15445/) (2024)
- [Large Language Models for Persian  leftrightarrow  English Idiom Translation](https://mlsanity.com/paper/2412.09993/) (2024)
- [ChatGPT as a Translation Engine: A Case Study on Japanese-English](https://mlsanity.com/paper/2510.08042/) (2025)
- [When the Gold Standard Isn't Necessarily Standard: Challenges of Evaluating the Translation of User-Generated Content](https://mlsanity.com/paper/2512.17738/) (2025)

## Abstract (arXiv)

This paper explores the influence of integrating the purpose of the translation and the target audience into prompts on the quality of translations produced by ChatGPT. Drawing on previous translation studies, industry practices, and ISO standards, the research underscores the significance of the pre-production phase in the translation process. The study reveals that the inclusion of suitable prompts in large-scale language models like ChatGPT can yield flexible translations, a feat yet to be realized by conventional Machine Translation (MT). The research scrutinizes the changes in translation quality when prompts are used to generate translations that meet specific conditions. The evaluation is conducted from a practicing translator's viewpoint, both subjectively and qualitatively, supplemented by the use of OpenAI's word embedding API for cosine similarity calculations. The findings suggest that the integration of the purpose and target audience into prompts can indeed modify the generated translations, generally enhancing the translation quality by industry standards. The study also demonstrates the practical application of the "good translation" concept, particularly in the context of marketing documents and culturally dependent idioms.