A Beginner’s Reading List for Large Language Models for 2026

A reading list for large language models (LLMs) aimed at beginners has been released, highlighting essential concepts and practical applications. The list is designed to help newcomers to the field of LLMs build a solid foundation and further their understanding of scaling and re-architecting models.

The article emphasizes the growing interest in LLMs, as more individuals seek to learn about the technology's inner workings. It presents a curated selection of readings that focus on fundamental principles, along with supplementary materials that cover advanced topics in LLMs.

Included in the reading list are three key resources for foundational knowledge. One such resource is the eBook "Foundations of Large Language Models" by Tong Xiao and Jingbo Zhu, which provides a comprehensive overview of core concepts. Another recommended reading is Pere Martra’s "Large Language Model Notebooks" course, which offers practical, hands-on learning experiences. Additionally, Dan Jurafsky and James H. Martin’s "Speech and Language Processing" ebook is suggested for a broader perspective on LLMs and related models.

The article also discusses two significant trends in the field: scalability and re-architecting LLMs. It points to the resource "How to Scale Your Model" developed by Google DeepMind scientists as a valuable guide on practical aspects of scalability. Furthermore, Pere Martra’s upcoming book "Rearchitecting LLMs: structural techniques for efficient models" is highlighted for its insights into customizing LLM architectures to meet specific needs.

Finally, the article mentions the importance of addressing bias in LLMs, particularly within transformer architectures. It notes strategies for optimizing models for bias-resilience while maintaining efficiency, underscoring the relevance of these topics for those engaging with LLMs in 2026.