An atlas for the LLM paper deluge

What it does This is the companion repository for the survey paper A Survey of Large Language Models. It catalogs papers, models, corpora, and tools into a taxonomy covering pre-training, adaptation tuning, utilization, and capacity evaluation. It also hosts evolutionary diagrams, prompt-design tips, and small-scale instruction-tuning experiments.

The interesting bit The repository treats bibliography as infrastructure: it tracks the actual volume of the research flood (arXiv LLM papers jumped from 0.40 to 8.58 per day after ChatGPT) and maps technical lineages with explicit solid/dashed evidence links for the GPT family and an open-source evolutionary graph for LLaMA variants. A recent addition covers long chain-of-thought reasoning, including test-time scaling and RL training methods used by DeepSeek-R1 and OpenAI’s o-series.

Key highlights

• Curated paper lists organized by pre-training, instruction tuning, alignment, in-context learning, chain-of-thought, and evaluation
• Visual timelines and evolutionary graphs for GPT-series and LLaMA-family models
• arXiv statistics quantifying the post-ChatGPT research explosion
• Small-scale instruction-tuning and ability-evaluation experiments on 7B LLaMA
• Accompanying Chinese introductory book for senior undergraduates and junior graduate students
• Recently updated coverage of long CoT reasoning, data distillation, and search-based synthesis

Caveats

• The repository is primarily a curated index and reading list, not a runnable framework or model zoo
• The authors explicitly note they need more computing power to expand the experiments beyond their current scope
• Some visual lineages (e.g., the LLaMA family tree) are manually maintained and necessarily incomplete given the sheer number of variants

Verdict Students and researchers who need a structured, taxonomy-driven entry point into the LLM literature will find this a valuable roadmap; developers hunting for drop-in code or pretrained weights should keep scrolling.