Investigating the Integration of LLM s into Trainee Translators’ Practice and Learning: A Questionnaire-based Study on Translator- AI Interaction (2025)

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.

## introduction large language models (llms) function as the foundation models of generative ai @xcite in performing text generation and language processing @xcite . very recently, the advent of llms has significantly impacted the translation industry. llms such as gpt-4, one of the latest in the generative pre-trained transformer (gpt) series, bert, and llama have quickly become popular tools in translators' workstations, reshaping established practices. in translation industry, there are also translation-specific llms or llm-integrated computer-assisted translation (cat) tools, such as trados copilot and wordscope, that are primarily designed for translation providers and professional translators. these ai-powered commercial tools provide professional translators with an all-in-one solution for their translation practice @xcite . unlike traditional nmt which is purely an approach to automatic machine translation @xcite , with their "inherent ability to understand, generate, and manipulate human-like text in a contextually relevant manner" @xcite , llms can be applied to a wide range of natural language processing (nlp) tasks, including question answering, summarization, text generation, and others. in other words, beyond their direct application to translation in the narrow sense, the high versatility of llms and their ability to be customized through prompt engineering can enable them to assist with various tasks across the entire translation workflow. the potential of llms in the translation industry warrants further exploration. in modern translation services, a translation project can, by and large, be divided into three phases: pre-production, production, and post-production, as outlined in the two standards, iso 17100:2015 and iso 11669:2024. while these standards are designed to provide guidance for translation service providers from a project management perspective, covering various administrative activities, many of the outlined tasks are also performed by, or involve, individual translators, even during the pre-and post-production stages. the key stages and tasks a translator may encounter throughout the entire process are summarized in it is not difficult to envisage llms being incorporated into many of these tasks or stages. when examining the task of translation in the narrow sense, it involves several functions where llms might be helpful, serving, for instance, as a dictionary, as an machine translation system providing reference translations, or even as a subject-matter expert by offering domain-specific knowledge, not to mention the fact that they could potentially be applied to more complex preproduction tasks, such as content analysis and terminology extraction, as well as in postproduction, where they might support feedback collection through the analysis of reviewer comments or client input. to date, much attention has been directed to claims of human parity in the translation abilities of llms, with a particular focus on their performance as machine translation systems-both in terms of evaluation @xcite and improvement @xcite . however, scant attention has been paid to the way in which translators, especially trainee translators, integrate llms into their daily workflows in practical terms. so far, @xcite have conducted a cross-sectional study exploring attitudes of translation teachers and languagerelated major students towards chatgpt and google translate, and the advantages and challenges brought by chatgpt. the results show that among four language-related majors, all translation students prefer google translate over chatgpt. another study conducted by @xcite investigates how translation students understand the benefits and challenges of using genai into their translation practice. while the study examined the functions of genai tools used by students in their translation practices, such as looking for background information, generating machine translation outputs, polishing human translations, and providing references for terminologies, its primary purpose was to explore trainee translators' perceptions of using genai in translation. however, the actual integration of llms into trainee translators' learning and practice remains underexplored. therefore, this paper addresses this gap by investigating the use of llms across the three phases of translation services and their broader impact on human-ai communication with a focus on trainee translators. to this end, the study examines when, in what contexts, and for what purposes trainee translators incorporate llms into their workflows, assessing their effectiveness and efficiency in different translation-related scenarios from a user-centered perspective. the study aims to identify the scenarios in which llms are most suitable and effective in students' translation workflow through a survey-based study. the results will serve as the initial step toward developing a large project: the design of an llm translator guide to help trainee translators choose the most suitable llm from among numerous options, including scenariospecific llms trained for different translation tasks and equipped with preset prompts. with its emphasis on translators in training, this research also seeks to contribute to the development of educational programs to better prepare future professionals for an ai-driven translation industry. ## literature review apart from the technically oriented research mentioned above, current scholarly work in translation studies focusing on translators and users mainly addresses the perception and reception of new technologies, particularly ai, by translators @xcite and their impact on the language services industry @xcite . more recently, a growing body of literature has begun to examine ethical concerns and sociotechnical effects associated with these innovations @xcite @xcite . the pedagogical applications and implications of genai have also begun to attract considerable attention, particularly in the context of computerassisted translator training @xcite . for example, pym and yu (2024) discuss the way in which translation technologies, including genai, can be integrated into language learning and translator training. similarly, @xcite dedicates an entire section to pedagogy, including insights into students' experiences with, and feedback on, the use of translation technology. nevertheless, research on human-ai interaction and the comprehensive application of llms throughout all three phases of translation services-pre-production, production, and postproduction-remains limited. while certain studies have examined prompting llms for translation tasks @xcite , the potential of llms to support functions beyond linguistic transfer through prompt engineering has received but little attention. yamada (2023) investigates chatgpt's customizability, for instance, but limits its analysis to prompt engineering for enhancing translation quality, so that further research is needed to examine its broader applications within a translator's workstation. ## conclusion as a preliminary step towards building an llm translator guide for trainee translators, this study has investigated the scenarios in which trainee translators rely on llms during their translation workflow, based on questionnaire research. the findings revealed that interactions between trainee translators and llms occurred mainly in the preproduction and production stages, where llms were used for tasks such as question answering, correction, content generation, and summarization. in contrast, the post-production stage saw less engagement with llms. moreover, despite the fact that trainee translators have already started to integrate llms into their translation workflow, their evaluation of the performance of llms revealed areas for improvement. this study has laid the groundwork for further research and development to optimize human-ai collaboration in translation. it is hoped that the final product-the llm translator guide-will enhance the competence of trainee translators and better prepare them for human-ai collaboration in future practice. if it works, the project will be extended to design a guide for professional translators as well, to improve their work efficiency and enable them to thrive in an aiintegrated translation industry.