mlsanityData to 7 Oct 2026

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

arXiv 2403.00127 ↗First posted 29 Feb 2024cs.CL cs.CY cs.HCcited by 12

Not on the map. Why?

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.

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

Builds on

7 of 18 references.

References · 7

  1. 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 344
  2. 2023Prompting Large Language Model for Machine Translation: A Case Studycited by 247
  3. 2023Is ChatGPT A Good Translator? Yes With GPT-4 As The Enginecited by 172
  4. 2022Prompting PaLM for Translation: Assessing Strategies and Performancecited by 144
  5. 2023How to Design Translation Prompts for ChatGPT: An Empirical Studycited by 31
  6. 2023Optimizing Machine Translation through Prompt Engineering: An Investigation into ChatGPT's Customizabilitycited by 11
  7. 2023Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clausescited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 7; 2026: 4.

12 papers.