@inproceedings{hao-zhang-2025-investigating,
title = "Investigating the Integration of {LLM}s into Trainee Translators' Practice and Learning: A Questionnaire-based Study on Translator-{AI} Interaction",
author = "Hao, Xindi and
Zhang, Shuyin",
editor = "Bouillon, Pierrette and
Gerlach, Johanna and
Girletti, Sabrina and
Volkart, Lise and
Rubino, Raphael and
Sennrich, Rico and
Farinha, Ana C. and
Gaido, Marco and
Daems, Joke and
Kenny, Dorothy and
Moniz, Helena and
Szoc, Sara",
booktitle = "Proceedings of Machine Translation Summit XX: Volume 1",
month = jun,
year = "2025",
address = "Geneva, Switzerland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2025.mtsummit-1.37/",
pages = "468--484",
ISBN = "978-2-9701897-0-1",
abstract = "In recent years, large language models (LLMs) have drawn significant attention from translators, including trainee translators, who are increasingly adopting LLMs in their translation practice and learning. Despite this growing interest, to the best of our knowledge, no LLM has yet been specifically designed for (trainee) translators. While numerous LLMs are available on the market, their potential in performing translation-related tasks is yet to be fully discovered. This highlights a pressing need for a tailored LLM translator guide, conceptualized as an aggregator or directory of multiple LLMs and designed to support trainee translators in selecting and navigating the most suitable models for different scenarios in their translation tasks. As an initial step towards the development of such a guide, this study, aims to identify the scenarios in which trainee translators regularly use LLMs. It employs questionnaire-based research to examine the frequency of LLM usage by trainee translators, the average number of prompts, and their satisfaction with the performance of LLMs across the various scenarios identified. The findings give an insight into when and where trainee translators might integrate LLMs into their workflows, identify the limitations of current LLMs in assisting translators' work, and shed light on a future design for an LLM translator guide."
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<abstract>In recent years, large language models (LLMs) have drawn significant attention from translators, including trainee translators, who are increasingly adopting LLMs in their translation practice and learning. Despite this growing interest, to the best of our knowledge, no LLM has yet been specifically designed for (trainee) translators. While numerous LLMs are available on the market, their potential in performing translation-related tasks is yet to be fully discovered. This highlights a pressing need for a tailored LLM translator guide, conceptualized as an aggregator or directory of multiple LLMs and designed to support trainee translators in selecting and navigating the most suitable models for different scenarios in their translation tasks. As an initial step towards the development of such a guide, this study, aims to identify the scenarios in which trainee translators regularly use LLMs. It employs questionnaire-based research to examine the frequency of LLM usage by trainee translators, the average number of prompts, and their satisfaction with the performance of LLMs across the various scenarios identified. The findings give an insight into when and where trainee translators might integrate LLMs into their workflows, identify the limitations of current LLMs in assisting translators’ work, and shed light on a future design for an LLM translator guide.</abstract>
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%0 Conference Proceedings
%T Investigating the Integration of LLMs into Trainee Translators’ Practice and Learning: A Questionnaire-based Study on Translator-AI Interaction
%A Hao, Xindi
%A Zhang, Shuyin
%Y Bouillon, Pierrette
%Y Gerlach, Johanna
%Y Girletti, Sabrina
%Y Volkart, Lise
%Y Rubino, Raphael
%Y Sennrich, Rico
%Y Farinha, Ana C.
%Y Gaido, Marco
%Y Daems, Joke
%Y Kenny, Dorothy
%Y Moniz, Helena
%Y Szoc, Sara
%S Proceedings of Machine Translation Summit XX: Volume 1
%D 2025
%8 June
%I European Association for Machine Translation
%C Geneva, Switzerland
%@ 978-2-9701897-0-1
%F hao-zhang-2025-investigating
%X In recent years, large language models (LLMs) have drawn significant attention from translators, including trainee translators, who are increasingly adopting LLMs in their translation practice and learning. Despite this growing interest, to the best of our knowledge, no LLM has yet been specifically designed for (trainee) translators. While numerous LLMs are available on the market, their potential in performing translation-related tasks is yet to be fully discovered. This highlights a pressing need for a tailored LLM translator guide, conceptualized as an aggregator or directory of multiple LLMs and designed to support trainee translators in selecting and navigating the most suitable models for different scenarios in their translation tasks. As an initial step towards the development of such a guide, this study, aims to identify the scenarios in which trainee translators regularly use LLMs. It employs questionnaire-based research to examine the frequency of LLM usage by trainee translators, the average number of prompts, and their satisfaction with the performance of LLMs across the various scenarios identified. The findings give an insight into when and where trainee translators might integrate LLMs into their workflows, identify the limitations of current LLMs in assisting translators’ work, and shed light on a future design for an LLM translator guide.
%U https://aclanthology.org/2025.mtsummit-1.37/
%P 468-484
Markdown (Informal)
[Investigating the Integration of LLMs into Trainee Translators’ Practice and Learning: A Questionnaire-based Study on Translator-AI Interaction](https://aclanthology.org/2025.mtsummit-1.37/) (Hao & Zhang, MTSummit 2025)
ACL