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

Sui He

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

T0 review · 1 major / 0 minor · reviewed 2026-05-24 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗

Pith's one-line read Translation briefs and translator or author personas add little to ChatGPT translation quality.

desk verdict The paper tests translation briefs and personas in ChatGPT prompts and finds limited gains, but supplies almost no experimental details to support the claim. read the letter →

arxiv 2403.00127 v2 submitted 2024-02-29 cs.CL cs.CYcs.HC

Sui He This is my paper

classification cs.CLcs.CYcs.HC

keywords promptengineeringmachinetranslationChatGPTbriefpersonapromptsstudieshuman-AIinteractionLLM

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper examines whether concepts from translation studies can be turned into prompts that improve machine translation. It compares prompts built around translation briefs, which specify task details for human translators, against prompts that assign the AI the role of translator or source-text author. Tests show these approaches help structure communication between people yet produce no clear quality gains when applied to ChatGPT. The work therefore points to a mismatch between tools designed for human-to-human translation and the requirements of human-machine translation.

What carries the argument

Comparative testing of prompt variants built from translation-brief elements and translator/author persona instructions.

What would settle it

A follow-up test in which revised versions of the same brief or persona prompts produce statistically higher scores on the same quality metrics than baseline prompts.

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Extended reading notes

Core claim

The paper claims that incorporating the conceptual tool of translation brief and the personas of translator and author into prompt design for translation tasks in ChatGPT has limited effectiveness for improving translation quality, even though these elements support human-to-human communication.

Load-bearing premise

The specific prompt wordings tested stand in for the full ideas of translation brief and persona, and the chosen quality metrics detect real differences in this AI setting.

Editorial extensions

If this is right

Elements drawn from translation briefs can clarify prompt structure but do not raise measured output quality.
Prompts that cast ChatGPT as translator or author yield no measurable quality advantage over simpler prompts.
Concepts developed for human translators must be reworked before they can support human-AI translation workflows.
Further study is needed on how translation studies ideas can shape GPT model training for translation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The gap may arise because language models parse role and context cues differently from human readers.
Hybrid prompts that combine brief elements with other engineering techniques could be tested next.
Training data that includes explicit translation-theory examples might reduce the observed limitation.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The main point is that translation briefs and translator/author personas add little to ChatGPT translation quality when used in prompts. The authors ran a comparison and reached a negative result on quality improvement while noting some value for human-style communication framing. That is the takeaway to carry away. The work is new in its narrow focus: it directly pits these two concepts from translation studies against each other in an LLM setting, an area the abstract correctly flags as underexplored. Prior prompting work has touched on role-playing or instructions, but this targeted pairing of brief and persona is a fresh empirical check. The paper does a reasonable job laying out why tools built for human-to-human translation might not transfer, and it ends with a sensible call for adapted concepts or using the ideas in model training instead. Those framing points are useful even if the outcome is negative. The soft spots sit in the missing methods. The abstract gives no information on languages, test sets, example counts, metrics, baselines, or how the prompts were worded and scored. Without those, the claim of limited effectiveness cannot be evaluated. The assumption that the tested prompts and quality measures actually instantiate the theoretical constructs is left unexamined. If the full paper contains clear reporting and statistical checks, that would address the gap; on the supplied text it does not. This paper is aimed at the small group working on prompt engineering for machine translation and on borrowing from translation theory. A reader already following that intersection might pick up the negative data point and the suggested research directions. It is not required reading for anyone outside that niche. The paper shows honest engagement with the relevant literature and a coherent question, so it deserves peer review. The idea is worth testing even if the current evidence is thin. I would send it out, with the expectation that referees will require full experimental details and perhaps stronger baselines before acceptance.

Referee Report

1 major / 0 minor

Summary. The paper explores the use of translation studies concepts—specifically translation briefs and personas (translator and author)—in prompt design for ChatGPT translation tasks. It concludes that while certain elements may aid human-to-human communication, their effectiveness is limited for improving translation quality in this LLM setting, and calls for adapting these tools to human-machine interaction paradigms and informing GPT model training.

Significance. If the empirical comparison holds under rigorous evaluation, the work would highlight gaps in directly applying human-centric translation concepts to LLM prompting, potentially guiding future prompt engineering and translation theory development for AI workflows. The absence of methodological details prevents assessing whether this advances the field substantially.

major comments (1)

[Abstract] Abstract: The main finding on limited effectiveness is stated without any information on test sets, number of examples, evaluation metrics, statistical tests, or baseline prompts, making it impossible to judge whether the data support the claim.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and the opportunity to respond. We address the single major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: The main finding on limited effectiveness is stated without any information on test sets, number of examples, evaluation metrics, statistical tests, or baseline prompts, making it impossible to judge whether the data support the claim.

Authors: We acknowledge that the abstract, as currently written, provides only a high-level summary and omits key experimental parameters. While abstracts are necessarily concise, we agree this limits immediate assessment of the claims. In the revised version we will expand the abstract with a brief clause summarizing the evaluation setup: the test sets and language pairs used, the number of examples, the primary metrics (automatic and/or human), any statistical testing, and the baseline prompts against which the translation-brief and persona conditions were compared. Full methodological details, including exact datasets, prompt templates, and evaluation protocols, remain in the body of the paper (Sections 3 and 4). revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The paper presents an empirical comparison of prompt formulations for ChatGPT translation tasks, drawing conclusions from experimental results rather than any derivation chain, fitted parameters, or self-referential definitions. No equations, ansatzes, uniqueness theorems, or load-bearing self-citations appear in the provided text. The central claim rests on observed differences in translation quality metrics, which are externally falsifiable and not constructed from the inputs by definition. This is the expected outcome for a non-theoretical empirical study.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The central claim rests on the domain assumption that translation quality differences can be detected and compared using whatever metrics the study employed, an assumption imported from NLP evaluation practice rather than derived in the paper.

assumptions (1)

domain assumption Translation quality can be meaningfully compared across prompt variants using standard evaluation practices from NLP and translation studies.
The conclusion of limited effectiveness presupposes that the chosen quality measures are sensitive enough to register real differences if they exist.

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Cite this review

Pith. "Pith review of Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts." pith.science (2026). https://pith.science/paper/2403.00127

@misc{pith2026240300127,
  author       = {Pith},
  title        = {Pith review of: Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2403.00127}},
  note         = {Machine review of arXiv:2403.00127}
}

read the original abstract

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.

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Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

[1]

Gao, Yuan, Ruili Wang, and Feng Hou

Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust, Abu Dhabi, United Arab Emirates (Hybrid). Gao, Yuan, Ruili Wang, and Feng Hou. 2023. How to Design Translation Prompts for ChatGPT: An Empiri- cal Study. arXiv. Gu, Wenshi. 2023. Linguistically Informed ChatGPT Prompts to Enhance Japanese -Chinese Machine Tr...

work page 2023
[2]

How Good Are GPT Models at Machine Trans- lation? A Comprehensive Evaluation. arXiv. Jiao, Wenxiang, Wenxuan Wang, Jen -tse Huang, Xing Wang, and Zhaopeng Tu. 2023. Is ChatGPT a Good Translator? Yes With GPT-4 as the Engine. arXiv. Lee, Tong King. 2023. Artificial Intelligence and Posthumanist Translation: ChatGPT versus the Trans- lator. Applied Linguist...

work page 2023
[3]

Routledge, London and New York

Introducing Translation Studies: Theories and Applications. Routledge, London and New York. Papineni, Kishore, Salim Roukos, Todd Ward, and Wei - Jing Zhu. 2002. BLEU: A Method for Automatic Eval- uation of Machine Translation . Proceedings of the 40 th Annual Meeting on Association for Computational Linguistics, pages 311–318. Philadelphia, Pennsylva- ni...

work page 2002
[4]

Proceedings of the Seventh Conference on Machine Translation (WMT) , Abu Dhabi

COMET -22: Unbabel -IST 2022 Submission for the Metrics Shared Task. Proceedings of the Seventh Conference on Machine Translation (WMT) , Abu Dhabi. Vilar, David, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2022. Prompting PaLM for Translation: Assessing Strategies and Per- formance. arXiv. Virtanen, Pauli, Ralf Gommers,...

work page 2022

Pith tools

Reviewed May 24, 2026 · model on record in the stance chip above.

[1] [1]

Gao, Yuan, Ruili Wang, and Feng Hou

Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust, Abu Dhabi, United Arab Emirates (Hybrid). Gao, Yuan, Ruili Wang, and Feng Hou. 2023. How to Design Translation Prompts for ChatGPT: An Empiri- cal Study. arXiv. Gu, Wenshi. 2023. Linguistically Informed ChatGPT Prompts to Enhance Japanese -Chinese Machine Tr...

work page 2023

[2] [2]

How Good Are GPT Models at Machine Trans- lation? A Comprehensive Evaluation. arXiv. Jiao, Wenxiang, Wenxuan Wang, Jen -tse Huang, Xing Wang, and Zhaopeng Tu. 2023. Is ChatGPT a Good Translator? Yes With GPT-4 as the Engine. arXiv. Lee, Tong King. 2023. Artificial Intelligence and Posthumanist Translation: ChatGPT versus the Trans- lator. Applied Linguist...

work page 2023

[3] [3]

Routledge, London and New York

Introducing Translation Studies: Theories and Applications. Routledge, London and New York. Papineni, Kishore, Salim Roukos, Todd Ward, and Wei - Jing Zhu. 2002. BLEU: A Method for Automatic Eval- uation of Machine Translation . Proceedings of the 40 th Annual Meeting on Association for Computational Linguistics, pages 311–318. Philadelphia, Pennsylva- ni...

work page 2002

[4] [4]

Proceedings of the Seventh Conference on Machine Translation (WMT) , Abu Dhabi

COMET -22: Unbabel -IST 2022 Submission for the Metrics Shared Task. Proceedings of the Seventh Conference on Machine Translation (WMT) , Abu Dhabi. Vilar, David, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2022. Prompting PaLM for Translation: Assessing Strategies and Per- formance. arXiv. Virtanen, Pauli, Ralf Gommers,...

work page 2022