Training Data for Large Language Model (2411.07715v1)

Abstract: In 2022, with the release of ChatGPT, large-scale LLMs gained widespread attention. ChatGPT not only surpassed previous models in terms of parameters and the scale of its pretraining corpus but also achieved revolutionary performance improvements through fine-tuning on a vast amount of high-quality, human-annotated data. This progress has led enterprises and research institutions to recognize that building smarter and more powerful models relies on rich and high-quality datasets. Consequently, the construction and optimization of datasets have become a critical focus in the field of artificial intelligence. This paper summarizes the current state of pretraining and fine-tuning data for training large-scale LLMs, covering aspects such as data scale, collection methods, data types and characteristics, processing workflows, and provides an overview of available open-source datasets.

• The paper highlights the essential role of diverse pretraining and fine-tuning datasets in enhancing large language model performance.
• The paper categorizes pretraining data into webpages, books, academic texts, code, social media, and encyclopedias to illustrate data diversity.
• The paper outlines rigorous processing workflows, including cleaning, deduplication, and synthetic augmentation, to improve dataset quality and model training efficiency.

Overview of LLM Training Data

The paper written by Yiming Ju and Huanhuan Ma explores the critical role of data in training large-scale LLMs, emphasizing the importance of both pretraining and fine-tuning datasets. With the significant performance improvements demonstrated by models like ChatGPT in 2022, there has been a paradigm shift in how these datasets are perceived, curated, and utilized. The paper systematically explores various aspects of training data, such as data scale, collection methods, types, characteristics, and processing workflows.

Pretraining Data

Pretraining data is the foundation upon which LLMs are built. These datasets are typically vast, covering a broad spectrum of domains and language forms. The paper categorizes pretraining data into several types based on their origin:

1. Webpages: These provide a massive volume of diverse language phenomena, offering insights into both everyday language use and academic discourse. They form a significant proportion of pretraining data due to their vastness and variety.
2. Books: While providing higher quality texts than webpages, book data gains significance due to its structured nature and comprehensive thematic coverage.
3. Academic Materials: Documents such as research papers and patents offer high-quality content rich in domain-specific terminology and formal language.
4. Code: Source code datasets are instrumental in nurturing models with enhanced logical reasoning and technical text generation capabilities.
5. Social Media: These data capture the prevalent informal communication styles and the dynamic use of language among users.
6. Encyclopedias: Known for structured and trustworthy information, these data sets contribute to a model's factual reliability.

Additionally, the paper details the processing techniques applied to pretraining data, which include cleaning, deduplication, and filtering to enhance data quality and model performance. Practices such as using the Common Crawl to gather web data and subsequent cleaning using frameworks like CCNet are highlighted for their efficacy.

Fine-Tuning Data

The fine-tuning phase tailors a pretrained model to specialized tasks, improving its capability to understand and generate task-specific output. The paper categorizes methodologies for constructing fine-tuning datasets:

• Manually Crafted Datasets: These are meticulously designed and evaluated for quality, ensuring alignment with task objectives.
• User Interaction Data: Real-world user interactions provide contextually rich, diverse scenarios that can enhance a model's real-world applicability.
• Dataset Augmentation: Utilizing existing datasets and generating more data using advanced models like GPT for data expansion and creation.
• Model-Generated Data: Models themselves can be used to generate new datasets, although care must be taken to ensure diversity and accuracy.
• Traditionally Converted Datasets: Historical archives of labeled data can be reformatted into the conversational and interactive paradigms suited for LLMs.

The fine-tuning datasets emphasize diversity, quality, and behavioral alignment with expected model outputs. The aim is often to leverage these datasets to guide models in task-specific applications such as dialogue, code generation, mathematical reasoning, etc.

Implications and Future Directions

This paper underscores the importance of high-quality data and the meticulous processes required to curate it. As the capabilities of LLMs grow, so does their dependence on varied and vast datasets that ensure they can understand and generate human-like text in multifaceted contexts.

Further developments in AI might explore more granular approaches to dataset curation, focusing on diversity and privacy. Additionally, innovative data generation methods, such as leveraging synthetic data, are likely to play a critical role in addressing concerns about data scarcity and maintaining competitive edges in model development.

Overall, the research presents an in-depth overview of the structures and processes governing LLM training data, highlighting the ongoing evolution and challenges in the domain.