Overview:

This comprehensive survey takes a deep dive into the world of Large Language Models (LLMs) by examining the datasets that form the backbone of these advanced AI systems. Covering aspects from pre-training corpora to fine-tuning and evaluation, the paper delves into the complexities of the datasets that enable the continued evolution of LLMs.

Key Insights:

• Datasets are categorized into five types: Pre-training, Instruction Fine-tuning, Preference, Evaluation, and Traditional NLP.
• In-depth analysis of 444 datasets, across 8 languages and 32 domains.
• Comprehensive statistics includes data sizes of over 774.5 TB for pre-training alone.

Opinion: I believe this survey to be instrumental in understanding the current landscape of LLM datasets. It not only provides valuable resources for researchers but also highlights areas where further diversification and expansion are necessary. The enormity of data reviewed here underscores the critical role of datasets in the success of LLMs and points towards the future where even more specialized datasets may emerge.