Principles of AI: LLMs (UPenn, Stat 9911, Spring 2025)

Instructor: Edgar Dobriban

This course explores Large Language Models (LLMs), from the basics to cutting-edge research.

Reference Materials

• Course Syllabus.
• Lecture Notes; Work in progress.

Lectures

Link	Topic
01	Motivation and Context
02	AI: Goals and definitions. The role of LLMs.
03	LLM architectures: attention and transformers.
04	Insight into transformer architectures.
05	Position encoding.
06	Specific LLM families: GPT, Llama, DeepSeek, LLM360.
07	Training LLMs: pre- and post-training, supervised fine-tuning, learning from preferences (PPO, DPO, GRPO).
08	Test-time computation: sampling, prompting, reasoning.
09	Empirical Behaviors: scaling laws, emergence, memorization, super-phenomena.

Student Presentations

• Calibrated Language Models Must Hallucinate by Georgy Noarov.
• Representations in Deep Neural Networks by Joseph H. Rudoler.
• First-Person Fairness in Chatbots by Varun Gupta.
• LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations by Ryan Chan.
• Representational Abilities of Transformers by Soham Mallick and Manit Paul.
• AI Control: Protocols and methods for deploying untrusted AI models by Davis Brown.
• Diffusion LLMs by Zhihan Huang and Kevin Jiang.
• Various forms of preference optimization by Tao Wang and Sunay Joshi.
• Adversarial Reasoning in LLMs by Mahdi Sabbaghi.
• Transformer Circuits: Mathematical Framework and In-context Learning by Hwai-Liang Tung & Yu Huang.
• Model Collapse by Xuyang Chen & Xianglong Hou.
• Weak to Strong Generalization by Behrad Moniri.
• Diagnosis of Alzheimer’s disease and other biomedical applications using vision transformers and large language models by Thomas Yu.
• Building Effective LLM Powered Autonomous Agents by Kewen Qu & Ruofan Wang.
• Post-Processing for Group Fairness: Applications to Classifiers and LLMs by Yuqing Lei & Binghao Yan.
• Distributed Training Strategies for Large Language Models by Tingyin Wang &, Siqi Chen.
• Chain of Thought Prompting by Yiwen Lu & Lu Li.
• Existential Risk by Yijun Fan.
• Jailbreaking Multi-Modal LLMs by Henry Shugart.
• Safety for LLM Agents by Zhanran (Chris) Lin.
• LLMs and Causal Inference by Kai Cooper.
• Efficient LLM Inference from the Sparsity Perspective by Yuzhe Gu.
• LLM alignment: additional topics in preference optimization by Zhuoran Xu and Shihan Chen.
• Uncertainty Quantification for Large Language Models: Part 1 by Anne Somalwar & Sima Noorani.
• How to Optimally Quantify Uncertainty for Risk-Averse Agents by Shayan Kiyani. video.

Additional Resources

Links to other courses

• Foundations of Large Language Models, U of Michigan, 2024
• Language Modeling from Scratch, Stanford, Spring 2024
• Recent Advances on Foundation Models, U of Waterloo, Winter 2024
• Large Models, U of Toronto, Winter 2025
• Advanced NLP, CMU, Spring 2025

Videos

• Andrej Karpathy's Neural Networks: Zero to Hero video lectures. 100% coding-based, hands-on tutorial on implementing basic autodiff, neural nets, language models, and GPT-2 mini (124M params).

Key papers

• The Llama 3 Herd of Models describes the Llama "open-weights LLM" developed by Meta. Possibly the highest information content anywhere about LLMs.
• DeepSeek-V3 Technical Report and DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning describe the open-weights DeepSeek V3 and R1 models, which bring together several innovations in training LLMs to make them achieve comparable performance to some top closed models.

Tutorials, books and book chapters

• The corresponding sections in the Understanding Deep Learning book. See also the associated tutorial posts: LLMs; Transformers 1, 2, 3; Training and fine-tuning; Inference
• Foundations of Large Language Models book

Workshops, conferences

NeurIPS, ICML, ICLR