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D. 참고문헌

이 교재에서 인용하거나 참고한 자료 목록입니다.

공식 문서

AI 서비스 공식 문서

  1. OpenAI. (2026). Prompt Engineering Guide. https://platform.openai.com/docs/guides/prompt-engineering

  2. OpenAI. (2026). GPT-5.4 Model Documentation. https://openai.com/research/gpt-5

  3. Anthropic. (2026). Claude Documentation. https://docs.anthropic.com/

  4. Anthropic. (2026). Claude's Constitution. https://www.anthropic.com/index/claudes-constitution

  5. Google. (2026). Gemini API Documentation. https://ai.google.dev/docs

  6. Google DeepMind. (2026). Gemini 3.1 Pro: Next-Generation Multimodal Model. https://deepmind.google/technologies/gemini/

학술 논문

대규모 언어 모델

  1. Vaswani, A., et al. (2017). "Attention Is All You Need." Advances in Neural Information Processing Systems, 30.

  2. Brown, T., et al. (2020). "Language Models are Few-Shot Learners." Advances in Neural Information Processing Systems, 33.

  3. Ouyang, L., et al. (2022). "Training language models to follow instructions with human feedback." Advances in Neural Information Processing Systems, 35.

프롬프팅 기법

  1. Wei, J., et al. (2022). "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models." Advances in Neural Information Processing Systems, 35.

  2. Kojima, T., et al. (2022). "Large Language Models are Zero-Shot Reasoners." Advances in Neural Information Processing Systems, 35.

  3. Liu, P., et al. (2023). "Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing." ACM Computing Surveys, 55(9).

  4. Yao, S., et al. (2023). "Tree of Thoughts: Deliberate Problem Solving with Large Language Models." Advances in Neural Information Processing Systems, 36.

AI 안전성

  1. Bai, Y., et al. (2022). "Constitutional AI: Harmlessness from AI Feedback." arXiv preprint arXiv:2212.08073.

  2. Ji, Z., et al. (2023). "Survey of Hallucination in Natural Language Generation." ACM Computing Surveys, 55(12).

  3. Weidinger, L., et al. (2022). "Taxonomy of Risks posed by Language Models." ACM FAccT 2022.

산업 보고서

AI 시장 및 트렌드

  1. McKinsey & Company. (2025). The State of AI in 2025: Generative AI's Enterprise Adoption.

  2. World Economic Forum. (2025). Future of Jobs Report 2025.

  3. Gartner. (2025). Hype Cycle for Artificial Intelligence, 2025.

  4. Stanford HAI. (2025). Artificial Intelligence Index Report 2025.

교육 및 윤리

  1. UNESCO. (2023). Guidance for Generative AI in Education and Research.

  2. OECD. (2024). AI and the Future of Skills.

법률 및 정책 자료

판례

  1. Mata v. Avianca, Inc., No. 22-cv-1461 (S.D.N.Y. June 22, 2023).
  2. AI 생성 가짜 판례 인용 사건

정책 문서

  1. European Union. (2024). EU AI Act.

  2. 대한민국 과학기술정보통신부. (2024). 신뢰할 수 있는 인공지능 실현 전략.

  3. 한국대학교육협의회. (2023). 생성형 AI 활용 교육 가이드라인.

도서

  1. Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

  2. Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/

  3. Bommasani, R., et al. (2021). "On the Opportunities and Risks of Foundation Models." arXiv preprint arXiv:2108.07258.

온라인 자료

교육 및 튜토리얼

  1. DeepLearning.AI. (2024). ChatGPT Prompt Engineering for Developers. https://www.deeplearning.ai/

  2. Google. (2024). Machine Learning Crash Course. https://developers.google.com/machine-learning/crash-course

  3. Microsoft. (2024). Generative AI for Beginners. https://github.com/microsoft/generative-ai-for-beginners

뉴스 및 분석

  1. MIT Technology Review. AI Section. https://www.technologyreview.com/topic/artificial-intelligence/

  2. The Verge. AI Section. https://www.theverge.com/ai-artificial-intelligence

데이터셋 및 벤치마크

  1. Hendrycks, D., et al. (2021). "Measuring Massive Multitask Language Understanding." ICLR 2021. (MMLU)

  2. Srivastava, A., et al. (2023). "Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models." TMLR 2023. (BIG-bench)

  3. Zheng, L., et al. (2024). "Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena." NeurIPS 2023.

참고 사항

인용 시 주의

  • 모든 참고문헌은 원본 출처를 직접 확인하시기 바랍니다
  • AI 분야는 빠르게 변화하므로 최신 버전 확인을 권장합니다
  • 학술 논문은 arXiv 또는 공식 학회 페이지에서 확인 가능합니다

추가 학습 자료

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