Large language models (LLMs) like ChatGPT, Claude, and Gemini are transforming the localization industry. While some fear job displacement, InText sees these tools as powerful allies for translators.
Here are key areas where LLMs are actively used:
LLMs help to automate tasks such as segmenting text, searching for similar phrases, and extracting key concepts, which speeds up the work of translators.
LLMs can consider cultural and linguistic nuances, drawing from multiple data sources to understand the context of a localization project and offer an accurate and culturally appropriate translation in the target language.
Our editors use LLMs as a tool to refine human-translated texts by checking for accuracy, cultural adaptation, and stylistic consistency. This speeds up post-editing and reduces costs.
LLMs can be used to test the quality of localizations, helping to identify errors, inconsistencies, or phrases that do not sound appropriate in the target language.
It’s important to note that LLMs are not yet fully used for translation, as they underperform compared to Neural Machine Translation (NMT) models. However, researchers expect expanded translation applications of LLMs in the coming years, with current focus on post-editing capabilities.
LLMs significantly speed up localization workflows, improve translation quality, and help to reduce costs. Still, human input remains essential, especially when deep understanding of cultural and linguistic nuances is required.
Please note that InText do not use LLMs without client’s approval.
You can learn more here.
