Large Language Models (LLMs) play a crucial role in the rapidly advancing field of artificial intelligence, particularly in natural language processing. The quality, diversity, and scope of LLMs are directly linked to their training datasets. As the complexity of human language and the demands on LLMs to mirror this complexity increase, researchers are developing new methods to create and optimize higher-quality, more diverse datasets.
Traditional methods for assembling datasets for LLM training typically involve gathering large text corpora from various public sources, such as the web and literature. This approach has its challenges, including ensuring data quality, minimizing bias, and representing lesser-known languages adequately.
According to a survey by researchers from South China University of Technology, INTSIG Information Co., Ltd, and INTSIG-SCUT Joint Lab on Document Analysis and Recognition, these challenges can be addressed through innovative methods of dataset compilation and enhancement. By leveraging both traditional data sources and advanced techniques, the researchers aim to improve LLMs’ performance in a wide range of language processing tasks.
One significant innovation is the development of a specialized tool that uses machine learning algorithms to refine the dataset compilation process. This tool sifts through text data to identify and categorize high-quality content and includes mechanisms to minimize dataset biases. These advanced methods have been shown to enhance LLM performance, especially in tasks that demand nuanced language understanding and contextual analysis.
The survey analyzed the landscape of datasets across five critical dimensions: pre-training corpora, instruction fine-tuning datasets, preference datasets, evaluation datasets, and traditional NLP datasets. This analysis revealed existing challenges and potential future directions in dataset development, underscoring the essential role of datasets in LLMs’ development lifecycle. Research shows that pre-training corpora alone exceed 774.5 TB, with other datasets including over 700 million instances.
The researchers also elaborated on the crucial data handling processes vital for LLM development, from gathering data to creating instruction fine-tuning datasets. A comprehensive methodology was outlined, encompassing data collection, filtering, deduplication, and standardization, which ensures that only relevant and high-quality data is used for LLM training.
Other points of focus in the survey included instruction fine-tuning datasets, the creation of various domain-specific datasets for model performance optimization across different tasks and domains, and the importance of evaluation datasets in testing models’ abilities in tasks such as natural language understanding, reasoning, knowledge retention, and more.
Looking forward, the researchers emphasized the importance of diversity in pre-training corpora, the need for high-quality instruction fine-tuning datasets and preference datasets for model output decisions. They highlighted the crucial role of evaluation datasets in ensuring the reliability, practicality, and safety of LLMs, underscoring the need for a unified framework for dataset development and management.
