Northeastern University: Foundations of Large Language Models
Summary: The content explores foundational methods and advanced techniques in large language model development, including pre-training, generative architectures like Transformers, scaling strategies, alignment through reinforcement learning and instruction fine-tuning, and various prompting methods.
Northeastern University: Foundations of Large Language Models
Summary of https://arxiv.org/pdf/2501.09223
Detail foundational concepts and advanced techniques in large language model (LLM) development. It covers pre-training methods, including masked language modeling and discriminative training, and explores generative model architectures like Transformers.
The text also examines scaling LLMs for size and context length, along with alignment strategies such as reinforcement learning from human feedback (RLHF) and instruction fine-tuning.
Finally, it discusses prompting techniques, including chain-of-thought prompting and prompt optimization methods to improve LLM performance and alignment with human preferences.
Related Articles
Students as Agent Builders: How Role-Based Access (RBAC) Makes It Possible
How ibl.ai’s role-based access control (RBAC) enables students to safely design and build real AI agents—mirroring industry-grade systems—while institutions retain full governance, security, and faculty oversight.
AI Equity as Infrastructure: Why Equitable Access to Institutional AI Must Be Treated as a Campus Utility — Not a Privilege
Why AI must be treated as shared campus infrastructure—closing the equity gap between students who can afford premium tools and those who can’t, and showing how ibl.ai enables affordable, governed AI access for all.
Pilot Fatigue and the Cost of Hesitation: Why Campuses Are Stuck in Endless Proof-of-Concept Cycles
Why higher education’s cautious pilot culture has become a roadblock to innovation—and how usage-based, scalable AI frameworks like ibl.ai’s help institutions escape “demo purgatory” and move confidently to production.
AI Literacy as Institutional Resilience: Equipping Faculty, Staff, and Administrators with Practical AI Fluency
How universities can turn AI literacy into institutional resilience—equipping every stakeholder with practical fluency, transparency, and confidence through explainable, campus-owned AI systems.