Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts
- URL: http://arxiv.org/abs/2403.00127v2
- Date: Sun, 28 Apr 2024 09:45:58 GMT
- Title: Prompting ChatGPT for Translation: A Comparative Analysis of Translation Brief and Persona Prompts
- Authors: Sui He,
- Abstract summary: This 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-to-human interaction.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Questionnaires for Everyone: Streamlining Cross-Cultural Questionnaire Adaptation with GPT-Based Translation Quality Evaluation [6.8731197511363415]
This work presents a prototype tool that can expedite the questionnaire translation process.
The tool incorporates forward-backward translation using DeepL alongside GPT-4-generated translation quality evaluations and improvement suggestions.
arXiv Detail & Related papers (2024-07-30T07:34:40Z) - Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective [72.83966378613238]
Under-translation and over-translation remain two challenging problems in state-of-the-art Neural Machine Translation (NMT) systems.
We conduct an in-depth analysis on the underlying cause of under-translation in NMT, providing an explanation from the perspective of decoding objective.
We propose employing the confidence of predicting End Of Sentence (EOS) as a detector for under-translation, and strengthening the confidence-based penalty to penalize candidates with a high risk of under-translation.
arXiv Detail & Related papers (2024-05-29T09:25:49Z) - (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts [52.18246881218829]
We introduce a novel multi-agent framework based on large language models (LLMs) for literary translation, implemented as a company called TransAgents.
To evaluate the effectiveness of our system, we propose two innovative evaluation strategies: Monolingual Human Preference (MHP) and Bilingual LLM Preference (BLP)
arXiv Detail & Related papers (2024-05-20T05:55:08Z) - Cross-lingual neural fuzzy matching for exploiting target-language
monolingual corpora in computer-aided translation [0.0]
In this paper, we introduce a novel neural approach aimed at exploiting in-domain target-language (TL) monolingual corpora.
Our approach relies on cross-lingual sentence embeddings to retrieve translation proposals from TL monolingual corpora, and on a neural model to estimate their post-editing effort.
The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment.
arXiv Detail & Related papers (2024-01-16T14:00:28Z) - Optimizing Machine Translation through Prompt Engineering: An
Investigation into ChatGPT's Customizability [0.0]
The study reveals that the inclusion of suitable prompts in large-scale language models like ChatGPT can yield flexible translations.
The research scrutinizes the changes in translation quality when prompts are used to generate translations that meet specific conditions.
arXiv Detail & Related papers (2023-08-02T19:11:04Z) - Decomposed Prompting for Machine Translation Between Related Languages
using Large Language Models [55.35106713257871]
We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations.
We show that DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.
arXiv Detail & Related papers (2023-05-22T14:52:47Z) - The Best of Both Worlds: Combining Human and Machine Translations for
Multilingual Semantic Parsing with Active Learning [50.320178219081484]
We propose an active learning approach that exploits the strengths of both human and machine translations.
An ideal utterance selection can significantly reduce the error and bias in the translated data.
arXiv Detail & Related papers (2023-05-22T05:57:47Z) - ParroT: Translating during Chat using Large Language Models tuned with
Human Translation and Feedback [90.20262941911027]
ParroT is a framework to enhance and regulate the translation abilities during chat.
Specifically, ParroT reformulates translation data into the instruction-following style.
We propose three instruction types for finetuning ParroT models, including translation instruction, contrastive instruction, and error-guided instruction.
arXiv Detail & Related papers (2023-04-05T13:12:00Z) - How to Design Translation Prompts for ChatGPT: An Empirical Study [18.678893287863033]
ChatGPT has demonstrated surprising abilities in natural language understanding and natural language generation.
We adopt several translation prompts on a wide range of translations.
Our work provides empirical evidence that ChatGPT still has great potential in translations.
arXiv Detail & Related papers (2023-04-05T01:17:59Z) - Bootstrapping a Crosslingual Semantic Parser [74.99223099702157]
We adapt a semantic trained on a single language, such as English, to new languages and multiple domains with minimal annotation.
We query if machine translation is an adequate substitute for training data, and extend this to investigate bootstrapping using joint training with English, paraphrasing, and multilingual pre-trained models.
arXiv Detail & Related papers (2020-04-06T12:05:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.