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LLM Data Guide & 6 Methods of Collection in 2024

LLM Data Guide & 6 Methods of Collection in 2024LLM Data Guide & 6 Methods of Collection in 2024

In the rapidly growing market of artificial intelligence (AI) and generative AI (Figure 1), one term that has taken center stage is ‘large language models’, or LLMs. These vast models enable machines to create content like humans. Data plays a foundational role in shaping the behavior, expertise, and range of these models. But how is this data accessed, especially given the emerging challenges?

This article provides a detailed guide on data for LLMs, helps business leaders decide which method of collection to choose, and provides some options for AI data collection services.

Figure 1. Generative AI market1

A graph showing the market size growth of generative AI from 2020 to 2030. The industry is projected to grow till 200 billion by 2023. This reinstates the importance of llm data for generative AI tools development.

What are large language models?

Large Language Models, or LLMs, are a subset of artificial intelligence, falling under the domain of natural language processing (NLP).

These large scale models are designed to understand natural language and produce human-like responses in different languages, achieving this through massive datasets and deep learning techniques.

Some of the most popular large language models include Generative Pre-trained Transformers (GPT series) and Bidirectional Encoder Representations (BERT).

How are LLMs impacting the tech industry?

LLMs are foundation models that power solutions like ChatGPT or Bard and are revolutionizing various sectors:

  • Conversational AI: Large language models, are at the heart of many customer service chatbots. They’re designed to understand user inputs and produce human-like interactions, making automated customer support more efficient and user-friendly.
  • Language translation: LLMs have revolutionized the way we approach language translation. Whether it’s translating everyday conversations or complex legal documents, these models provide quick and accurate translations, helping to overcome language barriers and foster global communication.
  • Programming: Some advanced LLMs have the capability to assist in software code generation. This not only makes the process of writing software code more streamlined but also enables business users, who might not have deep technical expertise, to participate in software development.
  • Scientific research: LLMs are playing a role in the world of science by assisting researchers. They can translate complex scientific jargon into more understandable terms and provide valuable insights, aiding in data interpretation and accelerating the research process.

Importance of data for LLMs

Large language models work by analyzing vast amounts of text data and learning patterns and structures in a language using techniques like neural networks. When given a prompt or question, they generate responses based on this learned knowledge, predicting the most likely sequence of words or sentences that should follow. The language model’s performance relies on adequate training Data. This data aids in:

  • Understand complex sentences: Context is vital, and having vast amounts of varied data allows LLMs to comprehend intricate structures.
  • Sentiment analysis: Gauging customer sentiment or interpreting user intent from text requires a broad range of examples.
  • Specific tasks: Whether it’s translating languages or text classification, specialized data helps fine-tune models for dedicated tasks like understanding contextually relevant text.

However, sourcing this data isn’t always straightforward. With growing concerns about privacy, intellectual property, and ethical considerations, obtaining high-quality, diverse datasets is becoming increasingly challenging.

How do we gather data for LLMs?

This section highlights some popular methods of obtaining relevant data to develop large language models.

Here is a table summarizing all the methods

Table 1. Comparing the 6 methods of obtaining data for LLMs

Managed data collectionLowMediumHighHigh
Automated data collectionHighHighMediumLow
Automated data
(Synthetic data)
Licensed data setsMediumHighLowLow
Institutional partnershipsMediumMediumHighMedium
Notes and disclaimer:
  • These are broad estimates and these may differ on individual projects’ characteristics.
  • We made this estimates based on our research and the data found from websites and B2B review platforms.

1. Crowdsourcing

You can partner with a data crowdsourcing platform or service as an effective means of gathering data for LLM training. Leveraging a vast global network of individuals to accumulate or label data. This method engages people from diverse backgrounds and geographies to gather unique and varied data points.


  • Access to a diverse and expansive range of data points. Since the contributors are located all over the world, the dataset is much more diverse.
  • Often more cost-effective than traditional data collection methods since there are no additional expenses.
  • Accelerates data gathering due to simultaneous contributions from multiple sources.


  • Quality assurance can be tricky with varied contributors since you can not physically monitor the work.
  • Ethical considerations, especially concerning fair compensation. Many companies like Amazon Mechanical Turk have been penalized for their unfair compensation practices in their crowdsourcing platforms.

Here are our top picks:


Clickworker is a crowdsourcing platform offering all sorts of AI data services. Its global network of over 4.5 million workers offers human-generated datasets for different use cases, including LLM development.


Appen is also a popular crowdsourcing platform offering human-generated AI data services. The company’s network consists of over 1 million workers. Check out these articles to learn more about Appen:

Here is a more comprehensive list of data crowdsourcing platforms

2. Managed data collection

Partnering with a data collection service can significantly enhance the process of training large language models (LLMs) by providing a vast and diverse dataset that is crucial for the development of these models. These services specialize in aggregating and organizing large volumes of data from various sources, ensuring that the data is not only extensive but also representative of different languages, regions, and topics. This diversity is essential for training LLMs to understand and generate human-like responses across a wide range of subjects and languages.


  • Quality and diversity of data: Data collection services often have access to high-quality and diverse datasets, which are essential for training robust and versatile language models.
  • Efficiency: Outsourcing data collection can save time and resources, allowing developers to focus on model development and refinement rather than on the labor-intensive process of data gathering.
  • Cost: This can be cheaper than in-house data collection or partnering with an institute.


  • Dependence on external sources: Relying on external services for data can create dependencies and potential risks related to data availability, service continuity, and changes in data policies.
  • Data privacy and ethics: Data collected by third-party services may raise concerns about privacy, consent, and ethical use, especially if the data includes sensitive or personal information.
  • Cost: Working with a partner can be more expensive than off-the-shelf datasets.

3. Automated data collection

Using automated data collection methods like web scrapers can be used to extract vast amounts of open-source textual data from websites, forums, blogs, and other online sources.

For instance, an organization working on improving an AI-powered news aggregator might deploy web scraping tools to collate articles, headlines, and news snippets from global sources to understand different writing styles and formats.


  • Access to a virtually limitless pool of data spanning countless topics.
  • Continuous updates due to the ever-evolving nature of the internet.
  • Much faster and inexpensive as compared to other modes of collecting language data.


  • Ensuring data relevance and filtering out noise can be time-consuming.
  • Navigating intellectual property rights and permissions can be challenging and expensive since many online platforms are now charging companies for scraping their data. If developers are scraping without permission, they are facing lawsuits.

Watch this video to see how OpenAI was sued for stealing data from popular authors for training its large language model GPT-3:

3.1. Automated data generation (i.e. Synthetic data)

You can also employ AI models or simulations to produce synthetic yet realistic datasets.

For instance, if a virtual shopping assistant chatbot lacks real customer interactions. It can use a natural language processing AI to simulate potential customer queries, feedback, and transactional conversations.


  • Quick generation of vast datasets tailored to specific needs.
  • Reduced dependency on real-world data collection, which can be time-consuming or resource-intensive.


  • Ensuring the synthetic data closely mirrors real-world scenarios can be challenging since even current powerful AI models sometimes can not provide accurate data.
  • Synthetic data cannot work on its own. You will still require human-generated data to add to the synthetic data.

Here is an article comparing the top synthetic data solutions on the market.

4. Licensed data sets

Directly buying datasets or obtaining licenses to use them for training purposes. Online platforms and other forums are now selling their data. For instance, Reddit recently started charging AI developers to access its user-generated-data.efn_note]Nicholas, Gordon. (2023). Reddit will charge companies and organizations to access its data—and the CEO is blaming A.I. Fortune. Accessed: 09/Oct/2023[/efn_note]


  • Immediate access to large, often well-structured datasets.
  • Clarity on usage rights and permissions.


  • Can be costly, especially for niche or high-quality datasets.
  • Potential limitations on data usage, modification, or sharing based on licensing agreements.

4.1. Institutional partnerships

Forming collaborations with academic institutions, research bodies, or corporations to gain proprietary datasets can significantly enhance the depth and quality of data available for specialized projects or research. Such collaborations enable access to a wealth of domain-specific information that might not be publicly available, providing a richer foundation for developing more accurate and effective tools or models.

For example, a firm specializing in legal AI tools could benefit immensely from a partnership with law schools and legal institutions, gaining access to an extensive array of legal documents, case studies, and scholarly articles. This would not only broaden the scope of their data pool but also ensure that their AI tools are trained on high-quality, relevant information, making them more efficient and reliable in legal contexts. These collaborations can also foster a mutually beneficial exchange of expertise and innovation, leading to advancements in both academic research and practical applications.


  • Gaining specialized, meticulously curated datasets.
  • Mutual benefits – while the AI firm gains data, the institution might receive advanced AI tools, research assistance, or even financial compensation.
  • The data is legal and not subject to lawsuits.


  • It can be challenging to establish and uphold trustful partnerships since different organizationss have different agendas and priorities.
  • Balancing data sharing with privacy protocols and ethical considerations can also be challenging since not all organizations trust others with their data.


With each method offering its unique advantages and challenges, AI firms and researchers must weigh their needs, resources, and goals to determine the most effective strategies for sourcing LLM data. As the demand for more sophisticated LLMs continues to rise, so too will the innovations in gathering the critical data that powers them.

Further reading

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External resources

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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