# Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts

arXiv: https://arxiv.org/abs/2403.00127 · HTML: https://mlsanity.com/paper/2403.00127/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [Prompting PaLM for Translation: Assessing Strategies and Performance](https://mlsanity.com/paper/2211.09102/) (2022)
- [How to Design Translation Prompts for ChatGPT: An Empirical Study](https://mlsanity.com/paper/2304.02182/) (2023)
- [Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizability](https://mlsanity.com/paper/2308.01391/) (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, 12 of 12)

- [The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs](https://mlsanity.com/paper/2509.03730/) (2025)
- [Principled Personas: Defining and Measuring the Intended Effects of Persona Prompting on Task Performance](https://mlsanity.com/paper/2508.19764/) (2025)
- [Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks](https://mlsanity.com/paper/2505.11665/) (2025)
- [Specification-Aware Machine Translation and Evaluation for Purpose Alignment](https://mlsanity.com/paper/2509.17559/) (2025)
- [Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection](https://mlsanity.com/paper/2502.06487/) (2025)
- [Testing LLMs' Capabilities in Annotating Translations Based on an Error Typology Designed for LSP Translation: First Experiments with ChatGPT](https://mlsanity.com/paper/2504.15052/) (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)
- [Arabizi vs LLMs: Can the Genie Understand the Language of Aladdin?](https://mlsanity.com/paper/2502.20973/) (2025)
- [Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intent](https://mlsanity.com/paper/2606.03259/) (2026)
- [Why do Large Language Models Fail in Low-resource Translation? Unraveling the Token Dynamics of Large Language Models for Machine Translation](https://mlsanity.com/paper/2605.07533/) (2026)
- [The Role of Prompt Language and Translation-Theory-Driven Prompts in Large Language Models: A Case Study on Spanish-Chinese Journalistic Translation](https://mlsanity.com/paper/2607.03160/) (2026)

## Abstract (arXiv)

Prompt engineering has shown potential for improving translation quality in LLMs. However, the possibility of using translation concepts in prompt design remains largely underexplored. Against this backdrop, the current paper discusses the effectiveness of incorporating the conceptual tool of translation brief and the personas of translator and author into prompt design for translation tasks in ChatGPT. Findings suggest that, although certain elements are constructive in facilitating human-to-human communication for translation tasks, their effectiveness is limited for improving translation quality in ChatGPT. This accentuates the need for explorative research on how translation theorists and practitioners can develop the current set of conceptual tools rooted in the human-to-human communication paradigm for translation purposes in this emerging workflow involving human-machine interaction, and how translation concepts developed in translation studies can inform the training of GPT models for translation tasks.