參考文獻（References）
[1] A Survey of Large Language Models. https://arxiv.org/abs/2303.18223
[2] Bridging minds and machines: a comparative study of AI and human rater agreement and reliability in educational assessment. https://link.springer.com/article/10.1007/s10639-026-13949-7
[3] Leveraging Expert Consistency to Improve Algorithmic Decision Support. https://arxiv.org/abs/2101.09648
[4] AI and shared decision-making: a systematic review, 2026. https://link.springer.com/article/10.1007/s00146-026-02955-5
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[10] Automated ETL Pipelines for Modern Data Warehousing: Architectures, Challenges, and Emerging Solutions. https://www.researchgate.net/publication/390849393
[11] ELT for LLMs: Enterprise Data Integration Architecture for GenAI Success. https://www.informatica.com/resources/articles/elt-for-llms.htm
[12] 發佈應用程式 | Microsoft Azure，https://azure.microsoft.com/zh-tw/partners/publish-your-app
[13] Copilot Studio overview. https://learn.microsoft.com/en-us/microsoft-copilot-studio/fundamentals-what-is-copilot-studio
[14] Microsoft Copilot Documentation and Responsible AI Guidelines. https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/architecture/responsible-ai
[15] Defensive Refusal Bias: How Safety Alignment Fails Cyber Defenders https://arxiv.org/abs/2603.01246
[16] AI Insights: Large language models (LLMs) Bias (HTML) https://www.gov.uk/government/publications/ai-insights/ai-insights-large-language-models-llms-bias-html
[18] Do Language Models Know When They're Hallucinating References? https://arxiv.org/abs/2305.18248
[19] Evaluating Large Language Models in Theory of Mind Tasks https://arxiv.org/abs/2302.02083
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