May 22

The content carefully polished by documentation engineers often encounters challenges such as confusing formats, version differences and process breakdowns in the localization process, resulting in deviations between the final output and the original intent. As a localization practitioner, this session will analyze the common pain points of technical documentation in localization communication and implementation from actual cases in different situations, and break the information barriers between documentation writers and localization teams. Nowadays, in the face of the reshaping of the localization industry by AIGC and big models, it is necessary to explore how to reasonably use AI translation, intelligent quality control and other tools to improve efficiency while ensuring professionalism, so that the technical content crosses the language barrier and accurately reaches the global users.

It builds a multi-agents collaboration ecology, and through the whole-link synergy of pre-translation analysis, translation decision-making, and post-translation optimization, it deeply reproduces the human translation thinking, and realizes flexible decision-making and quality leap in complex contexts. 1. “Multimodal Translation”: a unified framework handles multimodal text formats, including text, images and audio/video. 2. “Unlimited Context Window”: context extension can be realized to expand the context to the whole novel, the whole movie and TV drama, and the whole document project. 3. “Multi-Domain Dynamic Adaptation”: automatically recognizes vertical domains such as manufacturing/energy/medical/social sciences, automatically adjusts the style of translation generation, and accurately uses terminology. 4. “Global consistency”: terminology freezing, style migration, and memory bank are three in one, complementing the consistency guarantee mechanism .

With the rapid advancement of AI, Large Language Models (LLMs) are playing an increasingly important role in the localization industry. With strong language understanding and generation capabilities, LLMs show great potential in translation, content creation, and terminology management, driving the industry toward the intelligent stage.

LLMs enhance localization efficiency and quality in multiple ways. In machine translation and post-editing (MTPE), they significantly improve accuracy and fluency, reducing turnaround time. In terminology extraction and management, LLMs can automatically identify and standardize multilingual terms to ensure consistency. Additionally, they excel in localizing multimedia and marketing content, generating culturally adapted materials that support global business development.

Despite their advantages, LLMs also pose challenges such as data security, translation quality, and cultural adaptation. To address the issues, localization companies should enhance data privacy measures by considering private models or on-premise deployment to ensure data security. Meanwhile, the companies should also combine LLM outputs with human review to improve cultural relevance. Small and medium-sized businesses can leverage cloud-based LLM APIs to access advanced capabilities at a lower cost.

Looking ahead, LLMs will become more tailored and deeply integrated into localization workflows, enabling more efficient human-AI collaboration and enhancing globalization.