In today's fast-paced digital world, localization teams are under pressure to translate and adapt content more quickly and accurately than ever before. HubSpot’s senior manager Dierk Runne shares insights on how his team is leveraging AI, particularly large language models (LLMs), to facilitate faster and more efficient localization processes in their latest Q&A interview on HubSpot’s blog.
The localization team at HubSpot uses a combination of traditional neural machine translations (NMT) and emerging LLM technologies to manage their vast knowledge base. By updating hundreds, if not thousands, of articles frequently, Runne’s team ensures the time to translation is as short as possible, meeting the high demands of their evolving product.
Runne highlights a thrilling use case where Generative AI, via LLMs, is starting to play a role in refining and enhancing machine translation outputs for error detection. Interestingly, GPT-4 has shown promising results with over 80% accuracy in error detection during their testing phases. This blend of traditional NMT systems and cutting-edge LLMs showcases how localization workflows are becoming more sophisticated and responsive.
According to Runne, the biggest challenge remains integrating these AI-enhanced processes into existing workflows without disruption. Yet, the potential for LLMs to transform localization—ranging from immediate translation tasks to long-term improvements in translation memories—holds promising prospects.
As AI continues to evolve, it’s not solely about replacing human translators but rather about augmenting their capabilities. Localization teams like HubSpot’s are at the forefront of these advancements, enabling them to handle larger volumes of work with greater precision and speed, ultimately benefiting both the company and its global user base.
For more details on how HubSpot is merging traditional and AI-driven translation methods, read the full interview here: [How Localization Teams are Leveraging AI For Faster Turnarounds](Source).