awesome-instruction-learning

Why we included this project

Anyone starting out in large language model work will find this reading list a fast way to get their bearings on instruction tuning and following. The maintainers gather the recent papers and training data that matter in this area and group them by topics like corpora, taxonomy, analyses, and applications, with a companion survey tying the collection together. The datasets section is the part worth seeking out when you plan to build or fine-tune a model that obeys natural language instructions, because it leads straight to the actual data behind many instruction-tuned systems. Entries link directly to PDFs and related resources, so the list works both as a student onboarding roadmap and as a quick skim reference when you want the newest work in a narrower corner such as robustness, evaluation, or alignment.