The traditional methods of localization quality assessment have predominantly revolved around sampling methodologies. These methods leverage and make the assumption that the results from a sample can be extrapolated to represent the overall quality of a given asset. However, this reliance on sampling results in a crucial vulnerability: it requires precise accuracy in selecting the sampling target, a condition that is frequently undefined and uncontrollable, leading to potential inaccuracies and inconsistencies in quality assessment. Acknowledging these challenges, we have pioneered an alternative approach, utilizing Large Language Models (LLM) to enhance the precision and efficiency of localization quality assessment. Instead of a broad, undefined sampling method, our methodology strategically leverages LLM to pinpoint potential areas of quality concern, allowing us to conduct quality assessment samplings solely in targeted areas.

This targeted approach ensures that our assessments are concentrated on the most relevant sections, leading to substantial improvements in review turnaround times, cost efficiency, and overall quality. However, our use of AI extends far beyond mere translation verification. We employ AI to perform a comprehensive analysis that includes detecting the tone, style, and cultural relevance of the content. It reads developer comments, navigates through all related contexts, understands the definitions of bugs, and comprehends the nuances of various quality bars. In essence, our AI model serves as a meticulous examiner, scrutinizing every aspect of the content to ensure consistency and relevance. T

o further enhance the accuracy and reliability of our AI model, we have invested in extensive training using historical data, encompassing previous bugs, post-editing modifications, and customer feedback. This wealth of information allows the AI to tailor its analysis and results to the specific requirements of each project, ensuring that the insights and recommendations it provides are both precise and applicable.

Host organization: Globalization and Localization Association