Datasets for Large Language Models: A Comprehensive Survey
Yang Liu Jiahuan Cao Chongyu Liu Kai Ding Lianwen Jin
This paper embarks on an exploration into the Large Language Model (LLM)
datasets, which play a crucial role in the remarkable advancements of LLMs. The
datasets serve as the foundational infrastructure analogous to a root system
that sustains and nurtures the development of LLMs. Consequently, examination
of these datasets emerges as a critical topic in research. In order to address
the current lack of a comprehensive overview and thorough analysis of LLM
datasets, and to gain insights into their current status and future trends,
this survey consolidates and categorizes the fundamental aspects of LLM
datasets from five perspectives: (1) Pre-training Corpora; (2) Instruction
Fine-tuning Datasets; (3) Preference Datasets; (4) Evaluation Datasets; (5)
Traditional Natural Language Processing (NLP) Datasets. The survey sheds light
on the prevailing challenges and points out potential avenues for future
investigation. Additionally, a comprehensive review of the existing available
dataset resources is also provided, including statistics from 444 datasets,
covering 8 language categories and spanning 32 domains. Information from 20
dimensions is incorporated into the dataset statistics. The total data size
surveyed surpasses 774.5 TB for pre-training corpora and 700M instances for
other datasets. We aim to present the entire landscape of LLM text datasets,
serving as a comprehensive reference for researchers in this field and
contributing to future studies. Related resources are available at:
https://github.com/lmmlzn/Awesome-LLMs-Datasets.