Enterprise Localization in Times of Large Language Models with Centific’s Jonas Ryberg
Jonas Ryberg on the impact of LLMs, the future of multilingual content generation, and Centific’s new SaaS-based language AI platform.
Discover what’s new in the field of large language models, from ground-breaking research in academia to Big Tech’s latest releases.
Jonas Ryberg on the impact of LLMs, the future of multilingual content generation, and Centific’s new SaaS-based language AI platform.
New research suggests that integrating quality metrics as reward models into the machine translation pipeline can enhance the quality of the generated text.
New research reveals the best-performing machine translation evaluation metrics, identifies major challenges in metrics development, and suggests improvements.
New research shows that large language models are better translators of Arabic dialects than commercial machine translation systems but remain far from perfect.
Large language models offer new applications for traditional translation memory technologies. There’s a case for TMs to remain an important part of translation workflows.
A study introduces an approach to streamline translation between related languages, with the goal of enhancing trade efficiency and strengthening social connections, particularly in regions with related languages.
Microsoft Azure AI researchers explore the potential of large language models for automatic post-editing and find that LLMs are good but not great at it.
Brown University researchers reveal an issue with AI safety mechanisms in large language models involving low-resource languages.
A study demonstrates the ability of large language models to remove noise from datasets and underscores their potential for data cleaning.
Carnegie Mellon University researchers explore LLM effectiveness across 204 languages revealing their output limitations for low-resource languages.
Interest in automating machine translation quality estimation increases with the prevalence of large language models, but still has some way to go to be deployed at scale.
At SlatorCon Zurich, Dr. Sheila Castilho emphasizes the significance of contextual evaluation in assessing large language models and the need for a more rigorous evaluation approach.
Panelists from ServiceNow, LanguageWire, Busch Vacuum Solutions weighed the risks and opportunities of employing large language models in practical enterprise localization workflows.
Discover how AI and LLMs are reshaping innovation in business. Explore insights from Welocalize’s conversations with 40+ leaders at some of the largest global companies.
Monash University researchers show that large language models can do real-time machine translation and propose new ways for model fine-tuning.
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