rraghavkaushik/NLP-Reading-List
A curated collection of NLP and LLM resources. Covers essential papers and blogs on Transformers, Reinforcement Learning (RLHF, DPO, GRPO), Mechanistic Interpretability, Scaling Laws, and MLSys.
This is a curated collection of essential papers and blog posts that help AI researchers and machine learning engineers stay current with the fast-evolving field of Natural Language Processing (NLP) and Large Language Models (LLMs). It organizes key resources on topics like Transformers, Reinforcement Learning with Human Feedback (RLHF), and LLM system optimization, providing a structured way to grasp new advancements. Users can quickly find foundational and recent works to deepen their understanding or kickstart new projects.
Use this if you are an AI researcher or machine learning engineer looking for a structured, curated list of fundamental and cutting-edge resources to learn about NLP and LLMs.
Not ideal if you are looking for introductory material for beginners or hands-on code examples for implementing NLP models.
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Jan 27, 2026
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