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Unlocking Synergy: The Collaborative Power of LLMs and Human Translators in Localization

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💫 Short Summary

The webinar discusses the integration of AI models and LLMS in localization workflows, emphasizing the evolving role of linguists as language subject matter experts. It highlights the importance of AI in customer workflows, quality evaluation, terminology mining, and chatbot instruction, showcasing improvements in accuracy, efficiency, and cost savings. Challenges include bias, accuracy assessment, and computing resources. The implementation of AI in terminology management and chatbots significantly improves efficiency and translation quality. Source content analysis with AI enhances error detection and categorization, leading to improved translation workflows. Challenges in prompt engineering and error classification are addressed, emphasizing the need for clear source analysis. Future webinars on change management and innovation are promoted for interested viewers.

✨ Highlights
📊 Transcript
Highlights from the webinar 'Unlocking Synergy: The Collaborative Power of Large Language Models and Human Translators in Localization'.
Svaja Chti, a language research manager at Scientific, shared her journey within the company and the rebranding from Paka Edge.
The integration of AI generative models and LLMS into localization workflows was discussed, emphasizing the efficiencies and advancements brought by these technologies.
The presentation showcased the shift towards digital tech services and consultancy in the localization industry.
Expansion of Language Services at Company
The company is moving beyond localization services to offer a broader range of language services.
Linguists are now expected to become language subject matter experts, going beyond just translation and review tasks.
The evolving role of linguists includes prompt consulting and bias editing to meet the growing needs of the industry.
This transformation emphasizes the importance of human expertise in language services, complementing technology.
Importance of leveraging AI models in customer workflows and internal processes.
Language subject matter experts (SMEs) play a crucial role in ensuring efficiency.
Four use cases discussed: translation quality evaluation, term mining, chatbot instruction, and source content analysis.
Structured workflow incorporating AI is emphasized for these use cases.
Audience participation and questions are encouraged throughout the presentation to enhance value and interaction.
Localization workflow overview
Traditional workflow includes processing, converting, translating, and reviewing content.
Steps involve linguistic, technical, and internal checks, with pre-processing before customer delivery.
AI can be used for quality evaluation during machine translation or human assessment.
Real use case involved infusing AI into a customer's machine translation engine for marketing and online content in over 30 languages.
Importance of assessing reliability of AI models in quality evaluation.
Quality is subjective and varies based on customer expectations.
Language leads play a crucial role in ensuring alignment between AI evaluation and customer satisfaction.
Variations in accuracy among different language combinations were observed.
Feedback from language leads helped in identifying and rectifying discrepancies in evaluation, leading to improved overall quality assessment.
Improvement in Accuracy and Efficiency of Language Model.
Fine-tuning the model led to increased accuracy in all languages, particularly Russian and Korean.
Cost reduction and improved efficiency were achieved, aiding in better budget allocation for content review.
Targeted content review based on the model's reliability resulted in improved quality and cost savings.
Challenges in training AI models include assessing accuracy, aligning with customer expectations, and avoiding bias.
Utilizing trained pool of experts is essential, but using subject matter experts may introduce bias.
Computing resources like GPU and computing power are costly challenges to consider.
Despite the difficulties, investing in accurate models is worthwhile and leads to successful outcomes.
Use of AI in terminology mining for localization workflows.
Traditional manual methods for legal document localization into 50 languages were costly and time-consuming.
AI-infused workflows completed the process in four days, saving significant time without compromising quality.
The AI's accuracy was validated by a 100% approval rate from the customer's terminology team.
Aligning AI extraction criteria with existing rules and conditions was crucial for successful implementation, showcasing efficiency and cost-effectiveness.
Benefits of implementing AI in terminology management.
AI implementation resulted in cost reduction, increased term relevancy, saved time, more frequent glossary updates, and improved translation quality.
Challenges included avoiding term duplications and ensuring AI accuracy.
Different file formats and content sizes impacted AI efficiency and latency.
AI extracted around 60 unique terms, but many were already in the glossary. Chatbots were mentioned as useful for aiding linguists.
Implementation of AI chatbots in translation workflows improved efficiency and quality.
AI chatbots provided quicker access to data, reducing search time and assisting linguists in navigating style guides.
Translators spent 90% less time on research and were able to focus on translating and reviewing.
The chatbot showed minimal biases and privacy concerns, with high fluency and reasoning capabilities across multiple languages.
Accuracy and completeness of responses were analyzed for improvement.
Incomplete answers were due to complex and lengthy questions, causing the model to run out of tokens.
Lower accuracy was attributed to irrelevant questions from translators.
Benefits of improved responses include reduced queries, higher accuracy, and scalability.
Challenges include unpredictable queries and integration with existing translation tools.
Use of AI for source content analysis in localization process.
AI assessed content accuracy and categorized errors, improving translation quality.
Customer utilized a customized QA model for error categorization and quality assessment.
AI demonstrated high accuracy in error detection and categorization, reaching 93.53%.
Alignment with customer's quality framework and successful error identification showcased AI's effectiveness in improving translation workflows.
Challenges in error categorization and prompt engineering.
AI system had no false positives and low missed edits, but struggled with categorizing errors properly.
Ambiguity in error classification led to different interpretations by AI and human reviewers.
Clear source analysis and cleaning emphasized to reduce translation errors and queries.
AI system and human reviewers did not perform poorly overall.
Challenges in preventing model contamination and delivering poor quality text when learning from various editors.
Solution involves defining a pool of experts and selecting the best people for the task.
Prompt engineering challenges discussed, emphasizing the importance of finding a working prompt that can be used as long as other aspects remain unchanged.
Need for reassessment when changing models within the same family due to different training methods and data.
Presenter offers contact information for further discussion and feedback on the presentation.
Overview of upcoming webinars and events on change management and innovation.
Gala Innovates program is highlighted as a key event to participate in.
Viewers are encouraged to provide their name and email for more information.
The next webinar is set for May 21st and is only available to members.
The speaker expresses excitement to see attendees at future Gala events.