Large Language Models (LLMs) are transforming how we use artificial intelligence, moving beyond simple commands to a deeper understanding.
But what exactly are these powerful AI tools? How are they reshaping businesses?
At Grooper, our product uses LLMs. We use LLMs to give businesses solutions that save them tons of time and open new doors.
So dive in to explore the core concepts behind LLMs, their big benefits, and the challenges they still face. Based on our expertise, you will also see real-world applications and how LLMs actually work.
Contents:
- What is an LLM?
- What are the benefits of LLMs?
- What are the limits or problems of LLMs?
- What are LLMs used for?
- Examples or applications of LLMs
- How do LLMs work?
- How to build an LLM-powered chatbot
What is a Large Language Model?
A large language model (LLM) is a powerful form of artificial intelligence (AI), representing a type of machine learning called deep learning. These models use neural network architecture (often specifically a transformer) which enables them to understand the intricate relationships between characters, words, phrases, and sentences within human language.
This gives the AI the ability to interpret and answer questions in a more natural manner akin to human speech.
LLMs are trained on colossal amounts of data (usually text and code), which allows them to learn vast numbers of parameters. This extensive training empowers them to excel at various natural language processing (NLP) tasks and generating human-like text, which can include:
- Summaries
- Creative writing
- Factual content
For example, an LLM could analyze a long medical journal article and write up a concise summary. Or it could create a personalized greeting card based on a few input phrases from a human.
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What are the Benefits of LLMs?
LLMs can interpret and process human language in a more natural manner due to how they learn. So they can understand context in human language and answer questions which would trip up a non-LLM based AI.
For example, the question, "What is the best dessert to bring to a potluck?" would stump a more traditional AI. But an LLM would actually give a suggestion and elaborate on why it chose that answer. Just like a human.
With that in mind, here are the advantages of large language models:
- Speed - Compared to other forms of AI or humans, LLMs can generate accurate responses, drastically accelerating many workflows or even simple tasks.
- Operational cost savings - LLMs can handle many tasks, like data / document search, and analyzing data. This form of automation reduces operational costs.
- Flexibility - From creating a customer service bot for your external web site to an AI assistant that helps legal teams find clauses, LLMs can provide many solutions.
What are the Limits or Problems of LLMs?
Unfortunately, large language models face notable challenges and limitations. A big concern is their tendency to produce false information, a phenomenon often termed "hallucination."
LLM Hallucinations
How do LLM hallucinations happen?
The answers an LLM provides are only as reliable as the information it ingests. If you feed an LLM enough false information, it will give an incorrect answer.
Sometimes an LLM's bad responses can be plausible-sounding but factually inaccurate or even nonsensical. This can lead to misinformation, especially in critical domains like healthcare.
For example, you may have seen Google AI pulling answers to medical questions from Reddit. Answers that, when you read them, are obvious jokes or outright lies.
LLM Biases
Another big issue is bias. Since LLMs are trained on vast datasets, they can learn and push biases present in that training data.
This leads to skewed or discriminatory outputs. Addressing this requires continuous monitoring and careful data curation.
LLM Misunderstandings
LLMs also struggle with the complexity of human languages, sometimes not understanding nuances like sarcasm or cultural references. Their deep learning mechanisms, while powerful, lack true understanding or common sense reasoning.
LLM Data Privacy
Finally, data privacy is a critical issue. LLMs can unintentionally expose confidential or sensitive information if it was part of their training data or user inputs.
This risk means that strong security measures and careful handling of proprietary data are necessary. The balance between leveraging LLMs' power and easing these ethical and technical hurdles remains key to ongoing research and development.
What are LLMs Used For?
LLMs can be trained for a wide array of natural language processing tasks. This makes LLMs a core component of generative AI. The most common use is text generation, like:
- Articles
- Marketing copy
- Creative writing, like poems
LLMs are highly effective at text summarization, condensing lengthy documents into concise versions while preserving key information.
Also, LLMs can answer questions based on their vast knowledge bases, to provide instant and accurate information. They are also adept with programming languages, enabling them to write code and assist developers, as seen in examples like GitHub Copilot powered by OpenAI's models.
In customer service, LLMs enhance chatbots by allowing them to understand and respond to complex queries. They can also analyze sentiment in customer feedback, providing businesses with crucial insights into customer emotions and preferences.
Examples or Applications of LLMs
Examples of LLM AI used in the real world are: ChatGPT, Google Gemini or Bard, GitHub's Copilot, Meta's Llama, or Claude.
But what are the real-world examples of LLMs used in real world?
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Lease Document Analysis
One of the best LLMs uses in Grooper is to find named entities in lease documents, also known as lease automation. LLMs do phenomenal job of finding things like the name of a leasor or leasee in complicated documents.
For example, in Grooper we can type the question "Who is owner of this contract?" and get the correct answer in seconds. The big benefits are time saving and cost savings. A company no longer has to use staff employees (or lawyers by the hour) to perform slow manual work. Instead, they can type in a query and see the real-time results.
If a company is time restricted (such as oil and gas companies buying land leases), they can quickly find out important lease information, thanks to LLM AI.
Interested in this use case? Check out our video lease automation with LLMs.
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Automated Data Search and Extraction
LLMs can also be used to find and extract specific information. For example, one company in particular is using AI and LLMs in Grooper to extract 10 times more data than ever before. They previously only got three fields of data into their business system.
But now with Grooper, they are automatically extracting 30 fields of information. The same company is also now able to get their competitor's financial information off of public reports.
LLMs are adept at extracting data in any document with a wildly varying format. That's because AI LLMs don't require pre-set templates. Examples of this include:
- Price list
- Menus
- Invoices
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Individual Document Separation and Classification
People are using LLM AI in Grooper software to separate a group of dozens, hundreds, and thousands of documents into individual documents. Grooper also uses two different methods of document classification with LLMs to correctly organize documents.
How Do Large Language Models Work?
Large Language Models work on sophisticated machine learning ideas, especially deep learning. Basically, LLMs are built upon intricate neural networks, which is the 'brain' of an LLM.
Neural networks have layers of nodes that communicate to each other, much in the same way that neurons in a human brain do.
In a human brain, artificial neurons connect and exchange signals with each other. These networks are trained on large amounts of data (usually billions of pages of text) to learn patterns and relationships in language.
LLM Transformer Networks
A key innovation in LLMs is the transformer model, which employs a mechanism called self-attention. This allows the model to understand the context of words and phrases by weighing their importance relative to other elements in a sequence. It's similar to how we interpret words based on their surrounding text.
Through this process, LLMs learn to predict the next most probable word or sequence of words. An example of this is how Google search tries to predict the next word you are about to type.
LLMs ability to generate coherent and relevant text comes from ingesting a substantial amount of data. An LLM works to train itself and learn in regard to interpreting features and details with as little human aid as possible.
Training LLMs through transformer architecture and techniques like reinforcement learning from human feedback (RLHF) can improve an LLM by giving it human-like preferences. This makes an LLM more helpful and honest.
This leads to millions or billions of parameters that finely tune their responses. This helps LLMs generate nuanced language, even when it sees unique phrases of words. For more information, discover how to fine tune an LLM.
How to Build an LLM-Powered Chatbot
Begin by determining the use case for which the LLM will be used. Here's an easy guide to building an LLM-powered chatbot in Grooper:
- Setup: Make sure Grooper is installed and running. You need the Grooper Web Client and essential services like Activity Processing and Import Watcher up and running.
- Connect to OpenAI: Use Grooper’s LLM Connector to set up a connection to OpenAI. This lets Grooper use AI features like language detection.
- Create Your Chatbot:
- Go to the OpenAI Assistants platform and start a new assistant project. Give it a name and define its role as a Grooper help assistant.
- Enable "File Search" so that the assistant can access Grooper documentation and help files directly. Attach necessary files from the Grooper Wiki to your project.
- Test and Deploy:
- Use 'Detect Language' to ensure the chatbot can handle multiple languages by identifying the language of input queries.
- Test your assistant in the Grooper interface. Ensure it can provide accurate and helpful responses based on Grooper documentation.
- Engage the Assistant: Once ready, use the assistant in Grooper for answering requests. Set up guidelines for clear and concise responses, ensuring the chatbot is helpful and remains within its role as a Grooper support assistant.
This simplified process should help you quickly get an AI LLM-powered chatbot up and running in Grooper.
