<p>大型語言模型（LLM）的快速發展為各行各業帶來了重大機遇，但其成功部署需奠基於任務設計、資料集創建與評測這三大核心工作。基於我在TAIDE第一、二期計畫中的經驗，本次演講將深入探討在推動LLM落地過程中所面臨的挑戰，如如何確保資料品質和提升模型的可擴展性，並分享我們如何克服這些挑戰的具體方法。這些工作對於確保LLM能夠在不同領域實現擴展和應用至關重要。透過健全的資料集建設、有效的任務設計與嚴謹的評測，台灣正逐步邁向成為全球AI Foundry的目標，推動創新與技術領導力。本次討論也將闡明這些關鍵步驟如何為各行業及社會帶來深遠影響。</p>

<p>The rapid development of Large Language Models (LLMs) offers significant opportunities across industries, but their deployment relies on a strong foundation in task design, dataset creation, and evaluation. Based on key insights from the first and second phases of the TAIDE project, this talk will highlight the specific challenges we faced, such as ensuring data quality and scalability, and how we addressed them. These foundational efforts are crucial for scaling LLMs and ensuring practical applications in various sectors. By focusing on building robust datasets, designing impactful tasks, and establishing rigorous evaluation frameworks, Taiwan is well-positioned to accelerate its journey towards becoming a global AI Foundry, driving forward both innovation and leadership. This talk will also explore how these critical steps create far-reaching benefits, not only for industries but also for society as a whole.</p>