In the ever-evolving world of artificial intelligence, large language models (LLMs) continue to make waves with their transformative abilities. As more individuals seek to understand the intricacies of LLMs, a comprehensive reading plan becomes essential for beginners embarking on this learning journey. This article aims to provide a structured approach to building a beginner-friendly 2026 reading plan for LLMs, covering core concepts, scaling, re-architecture, and practical applications.
Introduction
The fascination surrounding large language models (LLMs) shows no signs of waning, as these models continuously redefine industries and push the boundaries of AI innovation. For those looking to delve into the world of LLMs, a curated reading list can serve as a valuable starting point. This article compiles essential readings to equip beginners with a solid foundation in LLMs, along with resources to explore scaling, re-architecture, and real-world applications.
The 2026 LLM Reading List You Were Waiting For
Below, we present a structured reading list divided into three key blocks, designed to cater to beginners and enthusiasts alike.
Conceptual and Practical LLM Foundations
To establish a strong foothold in understanding LLMs, we recommend delving into foundational resources that cover core concepts and practical applications.
- Foundations of Large Language Models by Tong Xiao and Jingbo Zhu offers a comprehensive exploration of LLMs, focusing on pre-training, generative models, prompting, alignment, and inference. This resource is ideal for gaining a deep theoretical understanding.
- Large Language Model Notebooks by Pere Martra provides practical insights into implementing LLMs, offering hands-on examples and Python notebooks for interactive learning.
- Speech and Language Processing by Dan Jurafsky and James H. Martin offers a broader perspective on LLMs and related models in the AI landscape, allowing readers to explore a wider array of concepts.
Scaling and Re-architecting LLMs
Once familiar with the foundational aspects of LLMs, it is crucial to delve into scaling and re-architecting trends to optimize model performance.
- How to Scale Your Model by Google DeepMind scientists provides valuable insights into scaling LLMs, covering practical aspects like TPUs, sharded matrices, and transformer math.
- Rearchitecting LLMs: structural techniques for efficient models by Pere Martra delves into the importance of re-architecting LLMs for specific needs or challenges, offering hands-on guidance on tailoring architectures for efficiency.
- Exploring bias in LLMs can uncover hidden biases and optimize models for bias-resilience, as discussed in this practitioner-focused resource.
Salient Research Studies and Application-Oriented Texts
For a deeper dive into research studies and real-world applications of LLMs, consider exploring the following resources:
- A study on LLM interpretability using probing classifiers and self-rationalization by Jenny Kunz.
- A Springer book focusing on LLMs in cybersecurity, highlighting their impact on defense strategies and reshaping the cybersecurity landscape.
- An exploration of LLMs in educational settings, offering insights into their role in learning environments.
- An interdisciplinary review of LLM applications across various fields, shedding light on their versatile use cases.
Wrapping Up
This curated reading list serves as a valuable resource for beginners looking to dive into the world of large language models in 2026. By exploring foundational concepts, scaling trends, and real-world applications, readers can gain a comprehensive understanding of LLMs and their evolving role in AI.
NOTE: For a more detailed list of resources and authors, please refer to the original article Here.
