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What are LLMs (Large Language Models)?
Large language models (LLMs) underpin the growth of generative AI. See how they work, how they're being used, and why they matter for your business.
Large language models (LLMs) underpin the growth of generative AI. See how they work, how they're being used, and why they matter for your business.
When you use generative AI to summarize a report or draft social media copy, large language models (LLMs) make it happen. LLMs are the underlying technology powering generative AI. And as they draw from more data, they can generate more accurate outputs. This is essential for businesses, which can use LLMs to give customers more relevant, personalized content.
Advancements in artificial intelligence (AI) fueled by LLMs also make it possible for companies to create and deploy AI agents. When prompted by customers or staff, these intelligent systems are capable of solving complex problems using memory, sequential reasoning, and self-reflection.
Let's dig into what makes an LLM, how these models work, and where they can benefit your business.
Large language models (LLMs) are the engines powering generative AI. LLMs can understand and respond to questions with natural language because they are trained on massive amounts of text data. These models are now used to create text and visual content, create summaries, and write new code.
Users interact with LLMs through prompts, questions and context written in natural language that are sent to the model. For example, you could ask a generative AI model to create a summary of this article. First, you would send the text of the article to your AI tool for it to ingest and analyze. Next, you'd write the prompt detailing what you were looking for. The LLM would them produce a high-level summary. The more data used to train the model, the more complete and accurate the results.
With the right data in place, there are many ways that businesses can use LLMs, such as having your sales team use AI for tasks like generating pitches — all using relevant customer data that speaks to pain points and preferences.
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Before you start getting too deep into your generative AI strategy, familiarize yourself with how this technology takes you from prompt to output. Large language models depend on three components: machine (and deep) learning, neural networks, and transformer models.
Machine learning (ML) algorithms instruct LLMs on how to collect data, discover connections, and identify common features.
Deep learning is a subset of ML that allows LLMs to learn with less human intervention and uses a probabilistic approach to improve accuracy. Consider an LLM that analyzes 1,000 sentences. Deep learning tools determine the letters "E", "T", "A", and "O" appear most often. From there, the model extrapolates (correctly) that these are among the most-used letters in English.
Neural networks, also called artificial neural networks (ANNs), are groups of connected nodes that can communicate with each other. These nodes are arranged into layers including input, output, and at least one middle layer — and allow LLMs to process information quickly. These networks are loosely based on the human brain’s neural networks but are far less complex.
Transformer models help LLMs understand language context. Using a technique known as self-attention, these models can analyze sentence structure and word choice to understand how elements of language relate to each other. This allows LLMs to better understand and process user queries.
LLMs understand text differently based on the models they use. Encoder-only models focus on making sense of text that is provided, while decoder-only models generate text based on a prompt. When you put them together — encoder-decoder — LLMs can understand and generate text, taking on language-driven tasks such as customer service or sales. For example, an LLM-driven AI chatbot might be used to answer customer questions about shipping times, product details, or pricing changes, freeing up human representatives to work on more strategic tasks.
There are many types of LLM agents, but no matter which one you use, training improves the accuracy and reliability of their outputs. Given that transformer-based neural networks can include billions of parameters, training is required to ensure parameters are correctly weighted and applied to queries. Different training models may be more or less effective depending on the complexity and use case of an LLM.
Zero-shot learning sees LLMs trained on the fly. Users ask questions and LLMs sort through connected data sources to find answers. Initial accuracy is typically low but improves over time.
In a few-shot approach, data scientists provide a small selection of relevant examples to help LLMs establish baseline connections. Few-shot training significantly improves accuracy in targeted areas.
Chain of thought (CoT) training walks LLMs through a simple reasoning process. Instead of asking a single question, CoT breaks it down into multiple parts. Here's an example:
Standard prompt:
Steve has 20 shirts. Half of his shirts are short-sleeved, and half of those shirts are blue. How many blue shirts does he have?
CoT prompt:
Steve has 20 shirts.
Half of his shirts are short-sleeved. This means he has 10 short-sleeved shirts.
Half of these shirts are blue, which means he has 5 blue shirts.
While the prompt itself isn't particularly complicated, CoT provides a step-by-step approach to problem-solving that shows an LLM how to answer the question. This approach can then be applied to other questions.
Fine-tuning and domain-specific models provide additional contextual information for targeted use cases. For example, a company looking to improve its analysis of social media sentiment might provide its LLM with detailed information about how to understand specific words and phrases within the larger context of social platforms.
In this type of model, rather than looking at the text itself, the model translates it into numbers — called vectors. By using numbers, computers can use machine learning to more easily analyze how words and sentences are placed together, making sense of the context and semantic meaning to identify relationships between the words.
In a multimodal model, LLMs are trained to use multiple data formats for input and output. Along with text, these formats may include audio, video, or image data.
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LLMs give AI agents the ability to converse in natural language, but that’s easier said than done.
Traditional bots require you to manually train natural language models to understand customer language and design dialogs. This process is extremely time-consuming and costly for a business, but LLMs offer simpler alternatives.
For instance, solutions like Agentforce — the agentic layer of the Salesforce platform — use pre-built skills (as well as low-code custom actions) instead of having you go through a lengthy training process. Agentforce also uses conversational AI, so interactions with agents will feel more natural than robotic.
Other common LLM use cases include:
The simple answer? Probably not.
The more complete answer? In most cases, building your own LLM is expensive, time-consuming, and unnecessary.
It's expensive because you need to invest in the expertise and infrastructure to develop a bespoke language model. It's time-consuming because you need to provide a wealth of training data, and ensure the training results in accurate outcomes. And it's unnecessary because, in most cases, you're reinventing the wheel.
Using pre-trained, open-source LLMs that come with built-in security guardrails often provides the best balance of performance and protection. Businesses can leverage the power of models trained using trillions of data points, without worrying that issues in code may lead to inadvertent compromise. You can supplement the LLM model’s information by using a RAG (retrieval-augmented generation), which combines your company’s most relevant and proprietary data.
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LLMs offer a host of advantages for organizations. These include the reduction or elimination of manual processes and the ability to discover new trends and insights using available data sources. However, to use LLMs effectively, businesses must recognize where they excel and where they may struggle.
Here's a look at some of the top advantages of LLMs, and the potential disadvantages of LLMs:
Two paths are likely for the future of LLMs: bigger, and smaller.
As deep learning algorithms improve and processors become more powerful, large language models will be capable of handling larger data volumes faster and more accurately than ever.
At the same time, expect to see the development of small language models that apply the same level of performance to smaller, more tightly controlled datasets. These smaller models offer a way for companies to define highly specialized parameters and receive high-accuracy outputs.
Large language models are inching ever closer to a complete, contextual understanding of communication. While oversight remains a critical component in LLM use, these models offer a way to bridge the gap between human insight and IT operations by allowing us to speak the same language.
Now that you have a deeper understanding of AI, as well as LLMs, you can take a tour of Agentforce. With Agentforce you can build autonomous AI agents using the LLM of your choice, helping your company get more done — offering a boost in ROI and productivity.
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