# Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions arXiv: https://arxiv.org/abs/2502.13780 ยท HTML: https://mlsanity.com/paper/2502.13780/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 119 references that are in the map. - First posted on arXiv 19 Feb 2025. ## Builds on (most cited first, 30 of 35) - [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018) - [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023) - [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020) - [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024) - [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024) - [Large language models that replace human participants can harmfully misportray and flatten identity groups](https://mlsanity.com/paper/2402.01908/) (2024) - [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022) - [AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap](https://mlsanity.com/paper/2306.01941/) (2023) - [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025) - [TICO-19: the Translation Initiative for Covid-19](https://mlsanity.com/paper/2007.01788/) (2020) - [Rethinking Model Evaluation as Narrowing the Socio-Technical Gap](https://mlsanity.com/paper/2306.03100/) (2023) - [The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions](https://mlsanity.com/paper/2310.12418/) (2023) - [Putting Humans in the Natural Language Processing Loop: A Survey](https://mlsanity.com/paper/2103.04044/) (2021) - [How Culture Shapes What People Want From AI](https://mlsanity.com/paper/2403.05104/) (2024) - [Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors](https://mlsanity.com/paper/2310.16924/) (2023) - [Neural versus Phrase-Based Machine Translation Quality: a Case Study](https://mlsanity.com/paper/1608.04631/) (2016) - [User Intent Recognition and Satisfaction with Large Language Models: A User Study with ChatGPT](https://mlsanity.com/paper/2402.02136/) (2024) - [Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?](https://mlsanity.com/paper/2312.02065/) (2023) - [What the Harm? Quantifying the Tangible Impact of Gender Bias in Machine Translation with a Human-centered Study](https://mlsanity.com/paper/2410.00545/) (2024) - [Unmet Needs and Opportunities for Mobile Translation AI](https://mlsanity.com/paper/2002.12387/) (2020) - [Backtranslation Feedback Improves User Confidence in MT, Not Quality](https://mlsanity.com/paper/2104.05688/) (2021) - [Toward More Effective Human Evaluation for Machine Translation](https://mlsanity.com/paper/2204.05307/) (2022) - [Human-Centric Research for NLP: Towards a Definition and Guiding Questions](https://mlsanity.com/paper/2207.04447/) (2022) - [What Else Do I Need to Know? The Effect of Background Information on Users' Reliance on QA Systems](https://mlsanity.com/paper/2305.14331/) (2023) - [Classist Tools: Social Class Correlates with Performance in NLP](https://mlsanity.com/paper/2403.04445/) (2024) - [Explaining with Contrastive Phrasal Highlighting: A Case Study in Assisting Humans to Detect Translation Differences](https://mlsanity.com/paper/2312.01582/) (2023) - [Designing Staged Evaluation Workflows for LLMs: Integrating Domain Experts, Lay Users, and Model-Generated Evaluation Criteria](https://mlsanity.com/paper/2410.02054/) (2024) - [Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?](https://mlsanity.com/paper/2209.02906/) (2022) - [ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness](https://mlsanity.com/paper/2403.18121/) (2024) - [Testing Untestable Neural Machine Translation: An Industrial Case](https://mlsanity.com/paper/1807.02340/) (2018) ## Built on by (most cited first, 8 of 8) - [QE4PE: Word-level Quality Estimation for Human Post-Editing](https://mlsanity.com/paper/2503.03044/) (2025) - [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025) - [Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation](https://mlsanity.com/paper/2505.24683/) (2025) - [Toward Machine Translation Literacy: How Lay Users Perceive and Rely on Imperfect Translations](https://mlsanity.com/paper/2510.09994/) (2025) - [Toward Machine Interpreting: Lessons from Human Interpreting Studies](https://mlsanity.com/paper/2508.07964/) (2025) - [Ouvia: A User-centered Framework for Measuring Usability of Speech Translation in Real-World Communication Scenarios](https://mlsanity.com/paper/2606.06177/) (2026) - [Bridging the Usability Gap: Lessons from Interpreting Studies for Machine Interpreting Design](https://mlsanity.com/paper/2606.16009/) (2026) - [Measuring User's Mental Models of Speech Translation in Human-AI Collaboration](https://mlsanity.com/paper/2606.24644/) (2026) ## Abstract (arXiv) Converging societal and technical factors have transformed language technologies into user-facing applications used by the general public across languages. Machine Translation (MT) has become a global tool, with cross-lingual services now also supported by dialogue systems powered by multilingual Large Language Models (LLMs). Widespread accessibility has extended MT's reach to a vast base of lay users, many with little to no expertise in the languages or the technology itself. And yet, the understanding of MT consumed by such a diverse group of users -- their needs, experiences, and interactions with multilingual systems -- remains limited. In our position paper, we first trace the evolution of MT user profiles, focusing on non-experts and how their engagement with technology may shift with the rise of LLMs. Building on an interdisciplinary body of work, we identify three factors -- usability, trust, and literacy -- that are central to shaping user interactions and must be addressed to align MT with user needs. By examining these dimensions, we provide insights to guide the progress of more user-centered MT.