Large Language Model Applications—Transforming the Way We Interact with Technology

Large language models (LLMs) have revolutionized artificial intelligence (AI) and the way people interact with much of today’s digital ecosystem. LLMs enable AI to be capable of highly sophisticated text generation, advanced machine learning, and a wide and ever-growing assortment of use cases in various domains. 

LLMs utilize natural language processing (NLP) to teach AI interfaces to understand human language via statistical modeling, deep learning, machine learning, and computational linguistics. The implications of LLM-derived technology have been, in many cases, transformative in a variety of industries and sectors. Read on to discover more.

What are large language models?

LLMs are a type of AI foundation model that programmers train on large data sets. This allows an AI interface to understand and replicate human language in a human-like way. You can train your LLM to perform a variety of tasks, such as text generation. When you input massive amounts of data into the model, it processes it through sophisticated neural networks to learn how to predict the more likely sequence of words. LLMs are predictive algorithms that can interact with you in sophisticated ways, though they don't “learn” in the same way humans do. 

LLMs have allowed generative AI frameworks to become accurate, fast, and user-friendly. By training them on extensive data sets and applying billions of parameters for accuracy, LLMs gradually learn to analyze patterns in linguistic data. As a result, LLMs learn how to replicate these patterns with increasing speed and precision.

Common applications of large language models

LLMs have a variety of use cases. Common applications of large language models include:

Text generation

LLMs allow you to generate a variety of different types of text, such as: 

• Blogs

• Articles

• Scripts

• Summaries

• Social media posts

If you don’t want to use an LLM to create an entire text, you can interact with it to get ideas you can then use to create the text yourself. As sophisticated as LLMs are, their output quality depends not only on the effectiveness of their training but also on the precision of the prompts you provide. 

An LLM’s sophisticated language skills allow it to craft nuanced text in various voices and tones, with retrieval-augmented generation (RAG) bolstering its capabilities. RAG is an AI training technique that ties an AI model to external sources of expert knowledge in order to supplement its prior training data. This helps keep an LLM up to date on changes in data, making its output more likely to be factually reliable. RAG also allows you to examine an LLM’s data sources so you can double-check it for accuracy. This helps alleviate the challenge of LLM and generative AI transparency. 

Chatbots and virtual assistants 

LLMs now power different types of chatbots and virtual assistants. You can use these to enhance customer support and speed up time to resolution. 

Virtual assistants allow you to perform basic customer service functions for yourself, such as: 

• Request information about a product or service

• Request refunds

• Lodge complaints

All of the above are basic tasks that an AI platform can automate, allowing human customer service workers to handle more complex interactions and improve overall customer experience and efficiency. Furthermore, LLM-derived chatbots can provide translations that are both faster and more accurate than traditional human customer service agents. LLMs also boost productivity and efficiency by operating constantly, allowing your business to provide consistent customer support without relying solely on human agents.

LLM-derived chatbots and virtual assistants utilize an omnichannel approach. That is, you can program them for use on a variety of platforms such as websites, kiosks, mobile apps, and social media. Users can, therefore, prompt them anywhere. 

Content summarization

You may eventually need to parse lengthy texts to locate pertinent information. Given this time-consuming task, you may overlook vital points. LLMs can perform this type of content summarization much more quickly and accurately. 

LLMs can analyze and summarize a variety of texts, such as: 

• Articles

• Reports

• Research papers

• Various corporate documents

• Customer history and information

Fast and accurate text summarization allows you to better understand the more meaningful content in a large document. Workers in the finance, media, and government sectors find LLM text summarization helpful. 

Due to their linguistic sophistication, LLMs are uniquely suited to document summarization, which can take two forms: 

• Extractive summarization involves an LLM deciding what’s important in a text and extracting key phrases. This allows for greater summarization accuracy. You can double-check your LLM’s work this way, too, since its output comes plainly and traceably from the document in question. 

• Abstractive summarization is essentially paraphrasing. Due to their proficiency in understanding text and language, LLMs can efficiently parse large documents, identify key points important to humans, and summarize this information in clear language, mitigating possible jargon 

Translation services

LLMs allow AI to understand and produce content in sophisticated ways. They can understand the cultural context to some extent, reply to queries coherently, and translate high-quality languages. 

Given that an LLM's translation accuracy depends on the quality and quantity of its training data, robust data sets are essential for accessing the benefits of fast and accurate translations in a globalized world. 

LLMs possess certain advantages over previous generations of machine learning-based translation technology. They can:

• Generate increasingly human-like text

• Operate on an entire document all at once, rather than segment by segment

• Perform language-related tasks in other languages, as well as translate between them

Sentiment analysis

This is important in the marketing realm in particular. Simply put, sentiment analysis is how you determine whether a customer’s review is positive, negative, or neutral. 

LLMs allow you to automate this process, scanning a variety of text types for sentiment analysis, such as: 

• Reviews

• Social media posts

• Emails

• Customer support interaction transcripts

By allowing AI to perform sentiment analysis, you’re mitigating potential personal bias and speeding up the process. Additionally, by analyzing sentiment analysis results in real-time, LLMs let you keep up with quick shifts in public opinion. This allows you to refine and deliver improved products with unprecedented speed. 

Choosing the right model 

Many LLM models exist, including: 

• ChatGPT

• Claude 2

• Llama 2

When choosing which LLM is right for you, you’ll want to consider your specific business needs. First of all, some programmers train LLM models on text alone—they don’t include audio or visual inputs in the training process. If you want to utilize an LLM trained on audiovisual data, you’ll want to select one with that capability. This type of multimodal LLM is particularly useful for developing conversational AI, the technology behind devices such as Siri and Alexa. 

An additional factor is cost. Some LLMs are expensive, while others are free and open-source. Some models operate on a subscription basis, while others use a "token" pricing structure. In this model, you are charged once you reach a certain output generation threshold. 

Choosing an LLM involves balancing accuracy from vast training data with usability; larger models may lead to slower response times.

Different LLM models, and their attendant AI platforms, have different capabilities. GitHub CoPilot, for example, writes code. If you don’t need an LLM that writes code, it may not be the right model for you. 

Some LLMs can be tailored by developers to meet specific use case requirements, and fine-tuning their training data helps to improve accuracy. This is domain specificity, and it comes with benefits such as increases in: 

• Precision

• Reliability

• Safety

• Efficiency

• User experience

Ethical considerations 

LLMs and AI come with ethical considerations. Developing rules around AI governance and responsible AI policy is important for any business using AI. 

Microsoft outlines a six-point responsible AI policy [1] that involves: 

1. Fairness

2. Reliability and safety

3. Privacy and security

4. Inclusiveness

5. Transparency

6. Accountability

Issues of bias are inherent to LLMs; their outputs reflect the biases present in their training data unless addressed during data selection. These biases, such as racism, sexism, and xenophobia, can lead to harmful outputs that some may perceive as truth instead of an error, with inaccuracies becoming more common in large training data sets. 

Another issue LLM users face is data privacy. LLMs can not only incorporate biased or factually inaccurate content but also inadvertently capture highly sensitive customer information, which can be vulnerable to malicious use by hackers. 

LLM transparency can address these issues to a certain extent. However, the technology's complexity makes clear explanations difficult, especially as reliance on LLMs grows in high-stakes industries like finance and medicine. 

Being transparent about your LLM’s flaws fosters public trust. You might choose to share information such as: 

• Your AI model’s training protocol

• Its approach to logic and reasoning, as far as you understand it

• Its training inputs and where they come from

• Your techniques for model evaluation

• How you handle bias and other issues of fairness

Learn more about LLMs and AI with Coursera

LLMs are complex. However, they exist at the heart of modern AI technology, the use of which continues to expand. You can learn more on Coursera, and AWS & DeepLearning.AI’s course, Generative AI with Large Language Models, could be a good place to start. If you'd like to start a career in AI, consider pursuing the Microsoft AI & ML Engineering Professional Certificate.