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.
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:
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.
Discover how practical, real-world AI can build data models from a single document and even generate fields and SQL queries. You will get expert tips for prompting LLMs and ensure your AI initiatives are built on data you can trust! GET THE VIDEO:
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:
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.
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.
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.
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.
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.
But what are the real-world examples of LLMs used in real world?
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.
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:
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.
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.
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.
This simplified process should help you quickly get an AI LLM-powered chatbot up and running in Grooper.