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Responsible Generative Artificial Intelligence

Period of duration of course
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Course info
Number of course hours
30
Number of hours of lecturers of reference
20
Number of hours of supplementary teaching
10
CFU 3
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Modalità esame

The exam will consist of a project and its presentation with a discussion.

Lecturer log

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Lecturer

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Docenti di didattica integrativa

Prerequisiti

This course has no prerequisites.

The course is designed for Master's students of the Scuola Normale Superiore (SNS) and PhD students enrolled in the National Ph.D. of Artificial Intelligence (University of Pisa)

and the Computational methods and mathematical models for science and finance (SNS). PhD students from other SNS doctoral programs with an interest in the topics covered by

the course are also encouraged to enroll.

Programma

The course is organized as follows in three modules: 


Module I – The Evolution of Generative AI

Evolution of NLP: from rule-based systems to Large Language Models (LLMs).


Module II – Technical Foundations of Generative AI

Transformer architecture, transfer learning, and large-scale computation.


Module III – Responsible Generative AI

Ethical, social, and technical challenges, including bias, hallucinations, and interpretability.

Evaluation and mitigation strategies.

Seminars, case studies, and hands-on sessions.

Obiettivi formativi

This course provides a comprehensive introduction to generative artificial intelligence, combining historical context, technical foundations, and responsible AI considerations. It begins by tracing the evolution of natural language processing (NLP), highlighting the key paradigm shifts that enabled the development of modern large language models (LLMs). Building on this perspective, the course explores the core technical components underlying generative AI systems, followed by an in-depth examination of the ethical, social, and practical challenges associated with their deployment. The course is enriched with seminars, case studies, and hands-on sessions to bridge theory and practice. It aims to equip students with both the technical understanding and critical awareness required to develop and evaluate generative AI systems responsibly.

Riferimenti bibliografici

Reference bibliography

  1. Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).
  2. Paaß, G., & Giesselbach, S. (2023). Foundation models for natural language processing: Pre-trained language models integrating media (p. 436). Springer Nature.
  3. Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 2019.
  4. Radford, Alec, et al. "Improving language understanding by generative pre-training." (2018).
  5. Brown, Tom, et al. "Language models are few-shot learners." Advances in neural information processing systems 33 (2020): 1877-1901.
  6. Wei, Jason, et al. "Chain-of-thought prompting elicits reasoning in large language models." Advances in neural information processing systems 35 (2022): 24824-24837.
  7. Kojima, Takeshi, et al. "Large language models are zero-shot reasoners." Advances in neural information processing systems 35 (2022): 22199-22213.
  8. Ouyang, Long, et al. "Training language models to follow instructions with human feedback." Advances in neural information processing systems 35 (2022): 27730-27744.
  9. Karpukhin, Vladimir, et al. "Dense Passage Retrieval for Open-Domain Question Answering." EMNLP (1). 2020.
  10. Dettmers, Tim, et al. "Qlora: Efficient finetuning of quantized llms." Advances in neural information processing systems 36 (2023): 10088-10115.
  11. Gallegos, Isabel O., et al. "Bias and fairness in large language models: A survey." Computational Linguistics 50.3 (2024): 1097-1179.
  12. Shuster, Kurt, et al. "Retrieval augmentation reduces hallucination in conversation." Findings of the Association for Computational Linguistics: EMNLP. 2021.
  13. Goodfellow, Ian, et al. "Generative adversarial networks." Communications of the ACM 63.11 (2020): 139-144.
  14. Bommasani, Rishi, et al. "On the opportunities and risks of foundation models." arXiv preprint arXiv:2108.07258 (2021).
  15. EMNLP 2024 Tutorial: Language Agents: Foundations, Prospects, and Risks https://language-agent-tutorial.github.io/


Moduli

Modulo Ore CFU Docenti
Responsible Generative Artificial Intelligence 20 3 Gizem Gezici, Fosca Giannotti
Supplementary teaching 10 0 Gizem Gezici
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