The backbone of large language models: understanding training datasets

Large Language Models (LLMs) continue to draw attention as some of the most transformative and impressive technologies in the artificial intelligence domain. LLMs enable natural language processing, human-like content generation, and coherent conversations. However, these models' capabilities are only as strong as the datasets on which they are trained.

Built on transformer architectures, Large Language Models (LLMs) learn to predict the next word or phrase in a sequence, capturing linguistic patterns, cultural nuances, and contextual knowledge from massive amounts of training data. The quality and scope of this data, to a large extent, define the revolution LLMs have brought to human-computer interactions.

A timeline of some of the most representative LLM datasets. Source: Datasets for Large Language Models: A Comprehensive Survey

Understanding LLM training datasets is essential for anyone working with AI or planning an ML-based project. They are more than mere inputs—they are the foundation for any modern AI system. This article explores the nuances of these datasets and their pivotal role in advancing AI, including their types and specific use cases.

What are LLM training datasets?

Training datasets are vast collections of textual data—whether structured or unstructured—typically drawn from diverse sources such as books, articles, web crawl data, and even code repositories. The data selection process is strategic: datasets must be comprehensive enough to provide broad general knowledge yet carefully filtered for quality, relevance, and ethical considerations, such as avoiding harmful content and mitigating bias.

Overview of the LLM training process: pre-training on broad and diverse datasets, fine-tuning with specialized, high-quality data and human feedback, and adaptation for task-specific applications using targeted datasets. Source: Large language models in medicine: the potentials and pitfalls

Training datasets are the lifeblood of large language models (LLMs), shaping their ability to perform complex text-related tasks. They are carefully curated repositories representing a broad range of topics, styles, and perspectives, enabling advancements in natural language processing tasks. These datasets are where the potential—and limitations—of LLMs are first defined.

A two-layer big data quality standard for assessing datasets across diverse applications. Source: The Challenges of Data Quality and Data Quality Assessment in the Big Data Era

The anatomy of LLM datasets

The key concept behind LLM datasets is tokens—the smallest units that the model processes. These can be words, subwords, characters, or other symbols, depending on how a particular dataset is tokenized. 

Tokenization converts raw text into tokens, which are then mapped to numerical representations through embeddings, enabling large language models to analyze them. For instance, the sentence "AI is transforming industries" might be tokenized into `AI`, `is`, `transform`, `##ing`, `industries`, where subwords like ##ing are used to improve efficiency and flexibility in representing language.

Tokenization rules in GPT-2 and GPT-4 highlight changes in handling contractions and punctuation. Source: Getting the Most out of Your Tokenizer for Pre-training and Domain Adaptation

Before tokenization, the raw text undergoes preprocessing to ensure the dataset is clean, consistent, and suitable for training. This process often involves removing irrelevant data, such as duplicate entries, advertisements, or excessive whitespace. The text is then standardized to a uniform format, ensuring consistent punctuation, capitalization, and special character usage.

Datasets frequently include metadata to enrich the data with additional context. Metadata might indicate the source type (e.g., scientific articles, novels, or blog posts) and attributes like language or publication date. This contextual information can influence how models weigh and process different inputs during training.

Typically, a dataset consists of three subsets: training, validation, and testing. Source: Putting machine learning to use in natural resource management—improving model performance

Together, these components create a robust framework, enabling LLMs to capture linguistic structures and nuances effectively while maintaining scalability and adaptability in real-world applications.

Open vs. closed datasets

The landscape of large language Model (LLM) training datasets is broadly divided into open and closed datasets, two categories with distinct characteristics. The choice between them depends mainly on the specific use case, available resources, and ethical or legal considerations. While open datasets are publicly accessible and emphasize transparency, the other category prioritizes exclusivity and customization.

Each type offers unique advantages and challenges while training large language models. Open datasets foster accessibility and innovation, making them a key driver for the democratization of AI. Conversely, closed datasets allow organizations to tailor the data for proprietary applications or industry-specific contexts. 

Examples of training data types for six Large Language Models. Source: The Limitations and Ethical Considerations of ChatGPT

This distinction significantly affects the development of language models, their operational deployment, scalability, and ethical alignment.

What are open datasets?

Open datasets are freely available data collections, while some also qualify as open-source datasets, granting additional freedoms for modification and redistribution. Often supported by governments, non-profit organizations, or open-source communities, they are designed to promote transparency, innovation, and collaboration. 

MINT-1T demonstrates how open-source multimodal datasets, combining diverse formats, enhance training data diversity for advanced AI models. Source: MINT-1T: Scaling Open-Source Multimodal Data by 10x

Due to their accessibility, they play a crucial role in lowering the barriers to AI research, enabling academic institutions and smaller developers to build competitive language models without extensive proprietary resources. Additionally, open datasets encourage community-driven improvements, allowing errors, biases, or gaps in the data to be identified and addressed collaboratively.

However, open datasets come with particular challenges. Their broad accessibility also means they may include outdated or irrelevant information. More critically, open datasets often lack proper curation, potentially exposing models to unintended biases or harmful content if not adequately filtered.

Open dataset examples

Open datasets have become essential for developing LLMs, providing extensive resources that span various industries and use cases. These are just a few prominent examples that illustrate their significance and diversity.

Common Crawl

Common Crawl is one of the largest open datasets. It has a permissive open-source license and offers snapshots of web pages, including their raw HTML, metadata, and text. Updated monthly, it enables LLMs to learn from an ever-growing body of content across industries like e-commerce, education, and publishing. 

Due to its size and coverage, Common Crawl plays a pivotal role in general-purpose AI development.

Wikipedia

Wikipedia serves as a treasure trove of structured and semi-structured information. With millions of articles across numerous languages, it provides rich context on historical, cultural, and scientific subjects. 

Industries like academia, content creation, and knowledge management rely heavily on data derived from Wikipedia for building AI systems that require accurate factual understanding and multilingual capabilities.

OpenWebText

Curated to mirror OpenAI’s GPT training data, OpenWebText focuses on high-quality web data sourced from publicly available content. By prioritizing coherent and relevant information, this dataset is particularly suited for building LLMs in fields like media, digital marketing, and social media analytics, where nuanced understanding and conversational tone are paramount.

PubMed Open Access Subset

Specializing in the healthcare and life sciences industry PubMed’s open-access dataset comprises millions of biomedical and scientific articles. It empowers LLMs tailored for medical research, clinical assistance, and drug discovery to operate with reliable and specialized knowledge.

Overview of PubMed Central (PMC), a free full-text archive supporting the creation of open datasets like the PubMed Open Access Subset for biomedical research. Source: About PMC

Project Gutenberg

Project Gutenberg is a repository of thousands of public domain books that enables LLMs to explore classical literature, philosophy, and historical texts. Ideal for the creative and education industries, this dataset contributes to AI applications in literary analysis, storytelling, and language learning.

Performance evaluation of models on another open dataset example: the IMDb Movie Reviews dataset, widely used for sentiment analysis. Source: Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Even such domain-targeted datasets mirror the transparency and accessibility of open data collections like Common Crawl. Altogether, they demonstrate the versatility of open data, supporting a broad range of industries and applications while driving AI innovation in an inclusive manner.

What are closed (curated) datasets?

Closed datasets are proprietary collections designed to address particular goals or constraints. They are rigorously curated and maintained to ensure the reliability and relevance of outcomes. 

Unlike open collections, these domain-specific datasets are not freely accessible and typically leverage exclusive sources, advanced curation processes, and strict ethical considerations to mitigate risks of bias or inaccuracy.

Examples of closed datasets

Organizations invest heavily in developing closed datasets, as they are critical for creating specialized AI applications in areas where accuracy, security, and domain expertise are paramount. Here are several examples created by enterprises prominent across the AI domain.

Google’s JFT dataset

Google’s JFT dataset is a massive collection of labeled images, including annotations such as object names and contextual descriptions. While primarily image-based, it influences AI language models by pairing visual content with descriptive text, aiding tasks like image captioning and vision-language integration. It finds applications in industries like advertising, autonomous vehicles, and content creation.

OpenAI’s GPT training data

OpenAI’s proprietary dataset combines web-based text, code, and high-quality curated sources designed to train models like GPT. While the exact composition remains undisclosed, the data emphasizes diverse, high-quality content free from harmful or irrelevant inputs. It powers applications in customer service, content generation, and professional tools.

Anthropic’s dataset

Anthropic’s proprietary dataset is designed with safety and alignment at its core, ensuring that models are less likely to generate harmful or biased content. Focused on ethical considerations, this dataset is tailored for sensitive applications in industries like legal, healthcare, and government, where precision and harm reduction are crucial.

DeepMind’s AlphaFold Dataset

AlphaFold’s curated dataset focuses on high-quality data for protein structures, advancing computational biology and drug discovery. This proprietary dataset empowers breakthroughs in healthcare, biotechnology, and academic research by enabling models to understand complex molecular interactions.

BloombergGPT Dataset

Bloomberg’s dataset, designed for its financial-specific GPT model, contains proprietary financial documents, news articles, and market data. This dataset supports domain-specific applications like financial forecasting, market analysis, and customer advisory, where timely and accurate information is critical.

Closed datasets ensure models excel in specialized applications, whereas open datasets might fall short due to a lack of domain-specific accuracy or contextual depth. While proprietary by nature, they represent the cutting edge of AI-driven innovation across diverse industries.

Advantages and challenges

The choice between open and closed datasets depends on the context of a particular project, including its goals and constraints. As you can see, both types have distinct benefits and limitations that influence their real-world applicability.

When to choose open datasets

Open datasets are ideal for projects that prioritize collaboration and scalability. They are especially suitable when:

• A smaller budget limits access to proprietary data.

• The project’s goal is to foster academic or non-commercial innovation.

• Building a generalized model requires broader knowledge.

When to choose closed datasets

Closed datasets excel when the system focuses on precision, exclusivity, and control. They are the better choice when:

• Industry-specific applications require tailored inputs.

• Security and compliance are paramount due to domain specificity.

• The project’s competitive advantages depend on proprietary resources.

Ethical and legal considerations in dataset use

Whether a project uses open or closed datasets, proper data handling is paramount for the business, both ethically and legally. 

Open datasets, in particular, pose unique challenges as their broad accessibility increases the risks of exposing sensitive information. Proper anonymization, compliance with regulations like GDPR, CCPA, or HIPAA, and robust review processes are necessary to protect individual privacy and prevent data misuse. 

Anonymization can preserve privacy but also potentially amplify biases in data characteristics. Source: Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients

Closed datasets, while more controlled, also require legal scrutiny to avoid issues like unauthorized data collection or intellectual property violations. Ethical obligations include minimizing biases and ensuring datasets represent diverse and inclusive perspectives. 

Both types of datasets can serve as responsible tools to foster innovation by prioritizing transparency, accountability, and compliance.

Training large language models: datasets handling best practices

Creating a reliable LLM for business applications starts with effective data handling. Best practices ensure that training datasets are comprehensive, clean, balanced, and representative of their intended use.

Two critical components of building a robust model training pipeline are thorough data preprocessing and striking the right balance between data quantity and quality.

Data preprocessing: cleaning and normalization

Data preprocessing is a critical step in preparing a training dataset. It involves refining raw input data to ensure uniformity and reliability across the entire collection. Effective cleaning and normalization allow models to focus on meaningful patterns, filtering out inconsistencies or noise.

Practical preprocessing steps:

1. Removing irrelevant or noisy data

Eliminate incomplete records, duplicate entries, or excessively short text snippets.

• A customer service chatbot dataset will require removing broken sentences, spam-like inputs, and nonsensical words to ensure its overall clarity and coherence.

Handling outliers

Identify and treat highly uncommon patterns in the data.

• In financial news datasets, flagging articles about extreme stock surges is important for their further relevance evaluation.

3. Standardizing formats

Ensure consistency in capitalization, punctuation, date formats, etc.

• Preparing multilingual finance datasets might involve converting all currencies into a common unit (like USD or Euro) and ensuring all dates use ISO 8601 formatting. 

4. Language-specific normalization:

Use stemming or lemmatization to normalize linguistic variations.

• In a sports-related content dataset, words like “running,” “runs,” and “ran” can be converted into their root form, i.e. “run.” 

Balancing data quantity and quality

While more data can improve a model’s performance, excessive low-quality information introduces noise and undermines its generalization ability.

The relationship between training dataset size and model accuracy in deep learning. Source: Wireless Powered Mobile Edge Computing Systems

Several strategies are typically used to balance data quality and quantity while preparing a dataset for LLM training.

1. Targeted data augmentation: Use synthetic data generation to balance underrepresented classes in datasets.

   • Example: For sentiment analysis datasets, generate additional examples of nuanced reviews (e.g., “This movie wasn’t bad, but it wasn’t good either”) to improve sensitivity to tone.

2. Stratified sampling: Sample data proportionally to represent various categories fairly.

   • Example: In creating a dataset for legal document summarization, ensure proportional representation of different case types (e.g., civil, criminal, commercial).

3. Active sampling: Focus on collecting data for edge cases or lesser-represented scenarios.

   • Example: In training a medical chatbot, include rare queries like symptoms of uncommon conditions, such as “What causes Kleine-Levin Syndrome?”

4. Iterative cleaning and eefinement: Evaluate model performance on test data and iteratively refine the training dataset.

   • Example: If an AI model performing product categorization struggles with edge cases like “plant-based milk,” adjust the training data to improve performance on such ambiguous terms.

5. Domain-specific balancing: Use domain knowledge to prioritize specific subsets of data.

   • Example: For a tourism-focused language model, emphasize data about local cultural etiquette over general weather descriptions.

Toward better training datasets

Training datasets can achieve the level of precision and consistency needed for LLM success by employing thorough preprocessing and a strategic approach to balancing quality and quantity. Whether cleaning raw inputs or refining underrepresented categories, these practices build scalable and reliable datasets across industries.

Final thoughts

The evolution of Large Language Models (LLMs) underscores the foundational role of high-quality datasets in defining their scope, utility, and potential impact. However, the rapid development of LLMs poses significant challenges, including the potential depletion of high-quality, human-generated data.

As shown in the projection below, the intersection of dataset size trends and the total available public text indicates that by 2028, the adequate stock of human-generated text could be fully utilized. This underscores the urgency of exploring alternatives, such as dynamic real-time data sources or synthetic data, to sustain innovation in AI development.

Projected intersection of the total available human-generated text and dataset sizes required for LLM datasets. By 2028, the text stock may be fully utilized if current scaling trends continue. Source: Will we run out of data? Limits of LLM scaling based on human-generated data

With every new breakthrough in AI capabilities, addressing these challenges and leveraging novel approaches to dataset generation will be critical for advancing how we approach training Large Language Models.