Year after year, technology continues to evolve, and it’s making a big impact on the customer service industry. Large language models (LLMs) are transforming the way companies interact with their customers — offering an automated but personalized approach to customer support tasks.
It’s estimated that AI and machine learning are used to automate around 40% of all customer interactions. Our AI Chat and Voice Assistant solutions help facilitate these interactions and enhance customer experiences.
In this guide, we’ll demystify LLMs so you can understand how they work, what they’re used for, and how you can put them to use for your customer support team.
A large language model uses artificial intelligence to perform natural language processing (NLP) tasks like translation, text generation, question answering, and sentiment analysis. If you’ve heard of (or used) ChatGPT, then you may understand the foundation of LLMs.
LLMs use deep learning — a complex type of AI-based machine learning — to process data in a way that closely resembles natural, human-like interactions. LLMs allow computers to understand, manipulate, and generate human language text.
As they’ve become more and more advanced, many teams have started allowing LLMs to streamline and automate even their human-facing processes.
Not only can LLMs handle repetitive routine tasks like appointment scheduling or FAQs, but they can also handle tasks like logical decision-making and appropriately route callers to the right representative. This is because LLMs have access to a massive quantity of data, making them useful for both routine and advanced tasks.
Generative AI refers to a broad category of artificial intelligence models that can create many different forms of content and information, like text, code, audio, and images —- all based on user input data.
LLMs are a type of generative AI model focused on NLP tasks — those that involve understanding and generating human language text, like translations or summarizations.
You can think of image generators like Midjourney as a generative AI tool, but tools like ChatGPT would be considered generative AI and use LLMs.
LLMs are built using what’s called transformer-based architectures. A transformer is a type of “neural network” architecture (yes, like a brain) designed by Google.
This network is composed of multiple layers, all of which work together to break down text into smaller pieces like words or characters, called tokens, to determine the relationship and meaning between each token.
The network tries to identify patterns in sequential data (like words that come after one another in a sentence) to extract context and meaning from them.
One layer within the neural network of LLMs consists of attention mechanisms. These allow the model to focus on specific parts of text to understand their context and sentiment. This helps the model formulate a human-like response based on a user’s input.
You can imagine a transformer architecture like a newborn’s brain — it takes in a stimulus and naturally starts identifying patterns and forming connections, hence the term “neural network.”
LLMs go through a vigorous “training” process where humans feed them large amounts of data to teach them how to understand and generate language. Just as a newborn listens to sounds adults make and tries to make sense of it all, LLMs do so in a much more complex way.
LLMs are trained on massive datasets of text, like books, articles, and even conversations, but then must be fine-tuned to provide tailored results depending on the task at hand.
The LLM training process goes through two key phases: pretraining and fine-tuning.
This two-step training process ensures your LLM can understand different inputs and generate the most accurate information for your customers.
Whether you know it or not, you’ve probably heard of LLMs and maybe even used them to help rewrite an email, brainstorm new ideas, or generate a blog post.
Let’s dive into a few of the most popular LLMs you may already be familiar with:
GPT-3, which stands for Generative Pre-trained Transformer 3, is currently the standard LLM model. However, you may have also heard of GPT-4, another LLM model with an even larger database than GPT-3. One of the most popular LLM tools — ChatGPT — is trained on GPT-3.
ChatGPT is an AI chatbot developed by OpenAI that provides users with responses based on their inputs. Aside from providing complex, human-like responses, ChatGPT keeps a log of your conversations to reference and inform future dialogue — just as (if not much better than) the human brain naturally would.
BERT (Bidirectional Encoder Representations from Transformers) is another popular LLM developed by Google. It was created to improve the comprehension of natural language nuances and can be used for an array of NLP tasks. BERT is trained to grasp and anticipate words and sentences that mimic natural language.
BERT can be applied to many applications but is most commonly known for its application on Google’s search engine. BERT uses natural language processing and sentiment analysis to tailor Google’s search engine results so that they relate better to a user's query. Before LLMs, computers weren’t able to comprehend the sentiment behind a query, but they can now better understand user intent and provide more accurate search results.
LaMDA (Language Model for Dialogue Applications) — one of Google’s conversational LLMs — was designed to improve conversational interactions. Google used human dialogue to train LaMDA so it has the capability to engage in natural, relevant, open-ended conversations.
In February 2023, Google announced the first version of Bard, its conversational AI tool powered by LaMDA. Similar to ChatGPT, you can input a prompt into Bard and it will output a response using the information it’s trained on. It can generate creative ideas and content and has language translation capabilities. Technology quickly evolves, and since first announcing Bard, Google has released another LLM model called PaLM2, which now powers Bard.
LLMs can be trained to perform a variety of natural language processing tasks. Here are a few non-exhaustive examples to paint a picture of the different ways you can use LLM-powered tools.
Now that we’ve covered the basics of LLMs and their general use cases, let’s dive into the specific ways LLMs can improve your customer support teams and enhance the overall customer experience.
LLMs are transforming the future of customer support. Their personalized and immediate responses save your team time and resources while helping increase customer loyalty.
At Smith.ai, we use LLMs to power our Voice Assistant and Web Chat solutions to help answer inbound calls & chats, qualify leads, and offer 24/7 customer support with accuracy and efficiency. Our live, virtual receptionists are also available around the clock to step into the conversations when complex or sensitive issues arise, so you can rest assured knowing a human is always there to help.
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