Rapid development of Large Language Models (LLMs) and similar automated approaches for translation tasks is increasingly affecting the landscape of translation technologies. As concerns about the outsourcing of translator work to these automated translation tools grow, it is increasingly crucial to gather insights from the translation community directly. To this end, we conduct an interview study with 19 professional translators working across 11 languages and 11 domains to understand their perspectives, experiences, and concerns with using translation technologies in their work. We find that translators are cautious when incorporating new tools into their workflow, with several expressing concerns that machine translation (MT) and LLMs are infringing on the necessary human aspects and verification processes of translation. Importantly, translators are worried that these tools have potential for harmful downstream effects due to compromising the human aspects of translation work. These findings demonstrate the need to develop translation technologies that directly serve translators'needs rather than replacing human translation. This can be done by focusing more on the assistive tools that emphasize the uncertain, social, and ultimately human character of translation, rather than automation.
Daniel Chechelnitsky, Sireesh Gururaja, Seyi Olojo et al.· 0 citations
Cognitive Chain-of-Thought (CoCoT) is introduced, a reasoning framework that structures vision-language-model reasoning through three cognitively inspired stages: Perception, Situation, and Norm, showing that structuring model reasoning through cognitively grounded stages enhances interpretability and social alignment, laying the groundwork for more reliable multimodal systems.
Eunkyu Park, Wesley Hanwen Deng, Gunhee Kim et al.· 3 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.