What Are Large Language Models (LLMs)?

Large language models (LLMs) are point technologies that can be used with a platform, UI, or service to generate human language for purposes such as generating creative marketing ideas, automating customer support with human-like responses, and analyzing complex legal documents in minutes. Businesses must, however, recognize the limits of this power as they integrate LLMs into their processes. In this blog post, we’ll explore what makes LLMs so versatile and how businesses can use them effectively in an increasingly competitive marketplace.

What is a large language model?

A large language model is an advanced AI model that can understand and generate human language. Designed to use language with remarkable proficiency across many applications, LLMs have the capacity to predict nuances and patterns in text based on an input prompt. This allows LLMs to deliver seemingly cognitive responses tailored to user needs, although at times their outputs can be inaccurate or unreliable.

How do large language models work?

Large language models (LLMs) are advanced AI systems that understand and generate human-like language by learning from vast amounts of text data. Let’s take a look at the fundamental elements that make up LLMs.

Machine learning and deep learning

At the core of LLMs is machine learning, a subset of artificial intelligence that lets systems learn from data and improve over time without being explicitly programmed. Within machine learning, deep learning plays a critical role, employing multi-layered neural networks to analyze vast amounts of data. Deep learning allows LLMs to capture complex patterns and relationships in language, so they can generate coherent and relevant text.

Neural networks

Neural networks are the building blocks of deep learning. Inspired by the human brain, these networks consist of interconnected nodes that process information in layers. In the context of LLMs, neural networks are trained on large datasets to recognize the relationships between words, phrases, and sentences—also called tokens. This training process involves adjusting the connections between neurons based on the input data. Models then can improve their understanding of language and generate more accurate responses.

Transformer models

 

Transformer models are a type of neural network architecture created to process sequential data such as language. Unlike previous models that processed words in order, transformers can evaluate the significance of each word in a sentence relative to all the other words for contextual understanding. This capability allows transformers to understand complex language structures for natural language processing tasks and generate rich responses.

Why are large language models important?

Because large language models (LLMs) can understand intricate language patterns, they can produce text for a versatile range of business tasks, such as drafting emails, generating reports, and even writing blog posts about LLMs—making them essential text and document creation tools across many industries.

LLMs also excel at other business needs, such as synthesizing large amounts of information for research and data analysis and translating text for international communication. In addition, the models are very adaptable, so organizations can customize them for specific business needs to improve efficiency and drive innovation.

Benefits of large language models

While LLMs do at times produce inaccurate or nonsensical outputs, their business advantages are numerous. Let’s look at a few of the main benefits.

Broad understanding of language

Because LLMs have been trained on vast datasets, they have broad understanding of language and a wide array of subjects. With their “world knowledge,” they can provide information about many topics, though not at the expert level. (Output from an LLM regarding a specific field should always be thoroughly evaluated.)

Creative content generation

With their linguistic abilities, LLMs can produce high-quality content using industry-specific terminology, specific communication styles, and unique brand voices. They can craft engaging, persuasive, and informative content, making them useful for both business communication and creative production.

Limitations and challenges of LLMs

While large language models (LLMs) are undeniably powerful, they are not without their limitations and must be adopted with care and caution. Let’s explore a few of these well-documented challenges.

Inaccurate outputs

One big limitation of LLMs is their tendency to produce "hallucinations”—responses that sound plausible but aren’t actually correct. Such inaccuracies can be problematic, particularly in high-stakes or highly-regulated fields like healthcare or legal services. Learn how ABBYY helps businesses get greater accuracy and quality from their LLMs with retrieval augmented generation.

High resource demands

LLMs require substantial computational power and memory to operate effectively. That can mean higher operational costs and a larger environmental footprint due to increased energy consumption. Particularly for smaller businesses, these financial and ecological implications can be significant.

Data quality dependency

LLMs can only perform as well as the data used during their training. If the training data is biased or incorrect, the outputs generated by the model can deliver unreliable responses. In addition, any real-world business usage of LLMs requires access to business knowledge. This is where rretrieval augmented generation is used to elevate data quality.

LLM use cases for business

Large language models are useful to a wide audience of individuals and groups, including students, researchers, content creators, business professionals, developers, and e-commerce platforms. Common uses cases for LLMs such as ChatGPT are text generation (writing activities such as blog posts, articles, poetry, and even code), text summarization, text translation, and question-and-answer.

For enterprises, large language models offer significant advantages across business sectors, but their effectiveness is greatly enhanced when combined with complementary AI technologies, such as intelligent document processing (IDP). ABBYY helps enterprises achieve better results from LLMs with IDP technologies that ensure that the LLM has access to the most current, verifiable facts from the business. Let’s look at some of the common business use cases that are possible when combining LLMs with intelligent document processing.

Financial services

By automating data extraction and summarization, LLMs can help financial institutions quickly process documents such as loan applications, contracts, and regulatory filings. They can also analyze and respond to customer inquiries to improve compliance and operational efficiency. For optimal results, however, LLMs should be part of a broader solution that integrates additional AI technologies to reduce the risk of inaccuracies.

Legal

Law firms and legal departments can use LLMs to streamline contract management and legal research. These models can analyze extensive legal documents, pinpoint relevant clauses, and generate summaries, enabling lawyers to make quicker, informed decisions. That said, since relying solely on LLMs can lead to inconsistent outputs and errors, legal businesses should incorporate additional measures to ensure accuracy.

Healthcare

LLMs can automate the creation of clinical documentation, such as patient notes and discharge summaries, for healthcare professionals. The models can also extract relevant information from electronic health records (EHRs) to provide insights for patient care and research. Nevertheless, LLMs alone do have the potential to generate unreliable information and should be integrated with other AI technologies to enhance the quality of outputs.

LLM examples

Each large language model has unique strengths and capabilities. Here are five notable examples that showcase the diversity within this technology:

• GPT-3 and GPT-4o (Generative Pre-trained Transformers): Developed by OpenAI, GPT-3 and its successor, GPT-4o, are known for their ability to produce human-like text.
• Claude: Created by Anthropic, Claude has a focus on safety and ethical practices in AI, with an approach to text generation that emphasizes user alignment and transparency.
• BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT is designed to understand the context of words in search queries, which helps improve the accuracy of search results.
• T5 (Text-to-Text Transfer Transformer): Also from Google, T5 sees natural language processing (NLP) tasks as text-to-text conversions, making it flexible for generating and reformatting text in various ways.
• XLNet: Building on BERT, XLNet uses a more advanced training method that enhances its ability to predict word sequences, resulting in better performance for a range of language tasks.
• Phoenix?

What are the different types of LLMs?

LLMs can be categorized in many different ways, including by accessibility. Open-source models like BLOOM and LLaMA, for example, are freely available for modification and use and promote collaboration, while proprietary models like GPT-3 and GPT-4o are developed by specific companies and are typically accessible through paid APIs. The most common way to categorize LLMs, however, is by their architecture and functionality.

Generative models

These LLMs are designed to create high-quality human-like text across various formats, including essays, code, scripts, and emails. Notable examples include GPT-3 and GPT-4.

Encoder-decoder models

Specialized for tasks such as machine translation and text summarization, these LLMs encode input text and decode it into the desired output format. T5, with its versatility in handling different NLP tasks, is a prominent example.

Encoder-only models

Focused on understanding the meaning of text, encoder-only models can perform tasks such as sentiment analysis and question answering. BERT, for example, uses its architecture to understand the subtleties of language.

Multimodal LLMs

These models can process and generate multiple types of data, including text, images, and audio. They’re used for tasks like image captioning, video analysis, and multimodal search to create a richer interaction between different forms of data.

In addition to these categories, there are specialized LLMs designed to excel in particular domains. For example, medical LLMs trained on healthcare literature can assist healthcare professionals with diagnosis and treatment.

How are large language models trained?

Large language models (LLMs) are trained using a massive amount of text data, such as books, articles, and websites. First, this data is collected and cleaned to remove any irrelevant information, then broken down into smaller parts called tokens that are easier for the model to understand. During training, the model learns to predict the next word in a sentence based on the words that come before it—a process that typically involves a lot of trial and error.

Once training is complete, LLMs are tested to see how well they can generate text, understand context, and perform various language-related tasks. If they perform well, they can be used in real-world applications, like chatbots or content generation tools. Some models can also learn continuously, allowing them to update and improve as they receive new information over time.

What is the difference between large language models and generative AI?

Large language models (LLMs) and generative AI are closely related yet different concepts. Basically, generative AI is a broader category that includes not just LLMs—which are specifically designed to understand and create human language—but any type of AI that can create new content via images, music, videos, or other formats. In short, while LLMs focus on language, generative AI encompasses a variety of tools that can produce different types of creative content.

The future of LLMs in business

While LLMs excel at generating text, relying solely on these general tools can result in incorrect or outdated information—and higher costs to boot. To address this, businesses are increasingly combining LLMs with purpose-built AI technologies, such as retrieval-augmented generation (RAG). RAG connects LLMs to external information sources like specialized databases, enhancing the model’s ability to deliver more nuanced, relevant responses while minimizing inaccuracies. For instance, in intelligent document processing (IDP), RAG acts as a bridge between the LLM and IDP for more precise outcomes tailored to specific business needs.

At ABBYY, we deliver tailored solutions that address unique business challenges—including the effective use of LLMs. Our Document AI technology uses purpose-built AI to enhance the capabilities of LLMs so businesses can accurately extract, comprehend, and use data from diverse document types. By customizing the AI's functionality to meet distinct operational needs, we transform general-purpose tools into bespoke solutions that drive organizational success.

Get in touch with us to find out how ABBYY can help you maximize the use of LLMs for improved efficiency and accuracy that drive your business forward.