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What Is a Large Language Model (LLM)?

By Michelle Knight on
large language model

A large language model (LLM) is an advanced artificial intelligence system that understands, processes, and generates human-like content. LLMs train on vast amounts of big data, allowing them to recognize complex patterns and structures to produce coherent, contextually relevant responses to a wide array of prompts.

At their core, LLMs utilize cutting-edge natural language processing (NLP), which is made possible through deep learning techniques. Consequently, LLMs perform a diverse range of data activities, from data summarization to creating visuals based on a description. See the “Applications of LLMs” section below for more details.

The emergence of LLMs has significant implications for data management. These models transform various types of content into data that can be extracted and used for multiple purposes. 

This capability makes LLMs foundational to other specialized AI applications, potentially reshaping how businesses handle information and make decisions. Consequently, LLMs promise to fundamentally reshape how businesses handle information and make decisions.

Large Language Models Defined

Large language models take on different technical and cognitive characteristics related to data management.

  • Technical features
    • They “operate using neuron-like structures that may link many different concepts and modalities together.”
    • They contain a lot of parameters at a massive scale.
    • They use the Transformer architecture “to weigh the significance of different words in a sentence, regardless of their positional distance from each other.”
  • Cognitive Features
    • They use self-supervised learning techniques to process vast amounts of unlabeled data without human intervention.
    • They have remarkable natural language processing capabilities, allowing them to understand and generate human-like text across various contexts and dialects. 
    • Another authority highlights the ability of LLMs to infer from context and understand text.

How LLMs Work

Building on these fundamental characteristics, let’s examine how LLMs operate from a data management perspective. LLMs harness data during two main phases: training and working.

During training:

  • Organizations collect big data and curate it. They provide this information to meet the large language model’s intended purpose.
  • This data is preprocessed:
    • It is cleaned so it is of adequate quality to train the model.
    • It is converted to a standard format to facilitate comparison and analysis.
    • Individual words are converted into word vectors, which are tokenized and broken down into their basics. Each token takes a unique number, that digitally represents context and meaning and is also a piece of data.
  • The large language model learns to predict what content follows next.
    • The data is split into training and validation sets.New examples and data are augmented.  
    • The model encodes the meaning it learned into vectors, data it can use.
  • These vectors are stored in a vector database that the LLM can access when prompted by another machine or human.

During its work:

  • The large language model receives a prompt or query from a human or a system.
  • The model understands this ask, by drawing on patterns and knowledge learned.
  • The model makes predictions on what to do next.
  • It does so through coherent and contextually relevant content.

How it does so is ambiguous or a black box, where the processes are not well understood. 

LLMs improve through fine-tuning. A pre-trained model gets domain or task-specific data. Through an iterative process and additional examples, the models get to a more desired result.

This fine-tuning process highlights how LLMs adapt and differ.

Types of LLMs

While LLMs share common principles, they vary significantly in their design and implementation. Depending on their architecture, accessibility, and specialization, each type of large language model has unique characteristics that impact data management. These are explained in more detail, below.

Data Architecture

As mentioned in the introduction, engineers build LLMs with a transformer-based architecture. But this infrastructure comes in different flavors. Here are three examples:

  • Autoregressive: Autoregressive models “generate text by predicting the next word given the preceding words in a sequence.” Examples include any GPT product, Chat GPT-4, Gemini, Mistral AI, and Claude. 
  • Autoencoders: Autoencoders classify and answer questions through the encoder part of the Transformer. Bidirectional Encoder Representations from Transformers (BERT) demonstrates this type.
  • Encoder-Decoder: Encoder-decoder models do machine translation and summarization through two components: an encoder for inputs and a decoder for outputs. Text-to-Text Transfer Transformer (T5) represent an encoder-decoder.

Accessibility

In addition to architecture, LLMs diverge according to their availability: open or closed.

  • Open-Source: Anyone can examine, modify, and distribute the code underlying the LLM. BERT is open-source. 
  • Closed (Proprietary): An organization or person owns the code in a closed model, and its inputs are restricted. A company’s LLM pilot is closed.
  • Hybrid: A hybrid model has a closed core model with opportunities to build customized tools from open-source code. Retrieval augmented generation (RAG) approaches add to the central LLM functionality through specialization. Claude is an example of a hybrid model.

Domain Specificity

Domain-specific LLMs can exist along with their architectural or accessibility characteristics. They impact the discovery of new insights and data quality. 

  • Zero-Shot Learning (ZSL): The LLM with zero-shot learning completes a task without labeled data for training. For example, an LLM identifies stripes on tigers and zebras, but not on polar bears. Data quality is hit or miss.
  • Fine-Tune Learning: In fine-tune learning, a pre-trained model learns from domain or task-specific labelled data. Through a slow iterative process with lots of examples, the models output higher-quality results.
  • Domain-Specific Learning: In domain-specific learning, the LLM undergoes training on datasets tailored to a particular use case or set of tasks. BloombergGPT specializes in completing finance-specific activities, offering the highest data quality for these.

A good combination of LLM leads to different types, each offering unique data management benefits.

Benefits of LLMs 

Large language models offer significant advantages to data professionals to work with data effectively. Here are three key areas where LLMs excel.

  • Data Governance (DG): DG describes the formalization of authority over data processes and activities.
    • Streamline Policy Creation: The LLMs develop dynamic framework components.
    • Monitor Compliance Adaptively: The models continuously track compliance and regulatory interpretation.
    • Improving Data Quality: The AI tool checks data quality and identifies potential mistakes.
  • Data Operations (DataOps): DataOps focuses on improving the communication, integration, and automation of data flows.
    • Discover: LLMS dynamically label data across repositories, which facilitates user exploration.
    • Store and Curate: The models tap usage logs to optimize storage capacity.
    • Process: Based on the use case profile, the AI tool determines the best computer resources to handle and deliver large volumes.
  • Business Intelligence (BI): BI describes the technologies and tools that analyze and report on various business operations.
    • Enhance Analytical Capabilities: Businesses can dive deeper into their data analysis and uncover new patterns and insights with LLMs.
    • Scale: LLMs handle larger datasets better and can scale with increasing volumes.
    • Save Costs: Models automate routine tasks, and save time and money.

For even more benefits of LLMs in data management refer to this article.

Challenges of LLMs

Despite these significant advantages, LLMs also present several challenges, such as:

  • Limited Reasoning: The LLM may not be designed for tasks requiring deep, logical reasoning or domain-specific knowledge.
  • Accuracy: AI tools may output incorrect or biased information due to poor data quality in the training materials.
  • Privacy/Security: The LLM can access vast amounts of personal data. Consequently, it needs resources to protect it appropriately and comply legally.
  • Transparency: As the model becomes more complex, the details about its decision-making become less clear. This “black box” nature between the data inputs and LLM outputs can challenge their validity.
  • Integration: The AI tool may have trouble connecting to existing data systems and workflows. Consequently, they may require significant customization and additional technologies to ensure seamless operation.
  • Maintenance: The LLM requires regular updates and retraining to stay relevant and effective.
  • Infrastructure requirements: An AI model can require extensive computational power and storage, leading to increased costs.

As the LLMs mature, these challenges will evolve. Some will resolve while new ones may emerge.

Applications of LLMs

While LLMs have some challenges, they have improved data management across many industries. Here are a couple of examples:

  • Data Discovery: Moderna used a large language model to augment drug discovery, speeding up pharmaceutical development to vaccinate people against COVID-19.
  • Data Quality: COIN, JP Morgan Chase’s LLM, identified and extracted data from legal documentation with a higher accuracy than human reviewers.
  • Data Integration: Shell uses AI to integrate data from a variety of sources, including sensors, logs, and environmental conditions. LLM technology enables the company to leverage this information and predict when equipment needs to be maintained.
  • Metadata Management Automation: Reuters has implemented  LLM to extract and classify key information from legal documents. This metadata makes legal information more findable and easier to maintain through metadata management.
  • Data Governance: One Trust’s AI-powered platform automatically monitors data handling practices to ensure compliance. Migros-Genossenschafts-Bund (Migros), a large Swiss supermarket, used One Trust to protect customer information.

For more examples of how LLMs have helped companies with data management, check out these promising AI use cases. 

The Future of LLMs

As these applications demonstrate, LLMs are already making a significant impact and have a lot of promise for the future. Here are some key improvements:

  • Increased Customization: Companies will continue to use LLMs as the foundation for small language models (SLMs). These domain-specific models will lead to more tailored insights and data management solutions.
  • Advanced Natural Language Understanding: The LLM will continue to improve in logical reasoning and integrate learnings from different media, like code, images, and videos. Consequently, data governance tools will improve by allowing richer conversations and more relevant applications of automated processes.
  • Real-time Learning and Adaptation: Models will move “from rigid, predefined schemas to more dynamic, automated, and flexible implementations.” Consequently, the most current data will become more accessible as discovery and ingestion processes improve.
  • Better Protection of Privacy and Security: The marketplace will see new LLM solutions and frameworks that address data privacy and security concerns. These advancements will open these tools up to corporate workers and partners. Trust will increase so that people and systems will have the correct data access.

These advancements will significantly impact how organizations manage, analyze, and derive value from their data. Prepare to see the LLM as a standard in the data professional’s toolkit.