Top Applications of Large Language Models (LLM)

In this article, we will explore various applications of Large Language Models and their transformative impact in different fields.


In recent times, Generative AI and Language Models are getting widespread attention worldwide. Initially, when Language Models were introduced, they were primarily associated with building intelligent chatbots capable of human interactions. However, today, we have the term "Large Language Models (LLMs)". What makes Language Models and Large Language Models different is that LLMs are large, massive, and trained on a very large corpus of text data. As a result, they are versatile and find applications in a diverse array of tasks. While chatbots remain a popular use case for LLMs, their potential extends far beyond that. In this article, we will explore various applications of LLMs and their transformative impact in different fields. But before that, let's discuss the idea of LLMs in small detail.

Large Language Models: What are they?

Large Language Models are advanced Artificial Intelligence (AI) programs that are designed to process and generate human-like text. You might be familiar with ChatGPT and the underlying model behind ChatGPT, GPT-3.5 is a state-of-the-art Large Language Model. These models are based on deep learning, a popular branch of AI that uses Neural Networks for developing intelligent systems. 

LLMs are primarily based on Transformers. These are a type of Neural Network architecture that has proven to be one of the successful algorithms in Natural Language Processing (NLP). These transformers are trained on very large text data from books, articles, websites, and other textual sources. For understanding the size, consider the scenario, suppose you have a text file on your computer that contains 30 articles copied and pasted from the internet, how much its size be? Maybe a few kilobytes or hardly megabytes, but can you imagine a dataset containing terabytes of text data? That's the size of the dataset used to train Large Language Models like GPT-3.5 and GPT-4.  

When Transformers-based models see these large amounts of data, it eventually understands the pattern behind the textual information, and finally learns the underlying meaning of the language. This is where Large Language Models emerge.

Apart from training with a large corpus of text, these models are built with millions or billions of parameters, making them extremely sophisticated. When built with a massive amount of parameters, LLM can extract highly complex patterns that exist in language, even common sense and reasoning capability. Taking the case of GPT-3, it has around 175 billion parameters, about GPT-4, has an impressive 100 trillion parameters. These models are extremely capable of understanding language, and reasoning. The incredible capabilities of these models and their advancements can really bring more applications and change the dimension of how humans interact with machines.

Applications of Large Language Models

Some of the popular applications of LLM range from Natural Language Understanding, Text Generation, Language Translation, Chatbots, and many more, let's discuss each of them one by one,

Natural Language Understanding (NLU)

Even though traditional chatbots can converse with us, they sometimes give random and unnecessary outputs. This is because these chatbots are just predicting the probability of what might come next. But Large Language Models and chatbots built upon them can simulate Natural Language Understanding. This means LLMs are capable of understanding the core meaning and structure of Natural Langauge.

Natural Language Understanding is one of the ongoing research in AI and NLP where the primary focus is the bridge the gap between human communication and machine understanding. When architectures like Transformers enter the stage, the field has improved ever before. LLMs like GPT-3, GPT-4, Gopher, LaMDA, and PaLM-2, are huge examples of state-of-the-art Natural Langauge Understanding. Taking the case of PaLM-2 by Google. They integrated this Language model into a real-world chatbot and found astonishing results where the robot is able to understand what someone said and perform tasks accordingly. It even understands what happens in the environment where it stands.

Natural Language Understanding can be applicable in many cases like sentiment analysis, intent recognition, text classification, etc. Sentiment analysis might be one of our favorite data science projects where we train algorithms to understand the sentiment behind a text description, but a well-trained LLM can perform a wide variety of sentiment analysis tasks without training it again.

Better Language Translation

A few years back, Recurrent Neural Networks (RNNs) are popular in Langauge Translation tasks. RNNs are a type of sequence-to-sequence (seq2seq) model where it converts a sequence of words in one language into another sequence of words in a different language. But there is a downside, Every language is different from one another and each language follows a different structure even if we are conveying similar ideas. This came to be one of the big challenges in Language Translation tasks.

A Good Langauge Translation arrived when Google introduced Transformer based language models. These models work based on a self-attention mechanism where the model finds relevant words from a large corpus of text to understand what the entire text is all about, this reduced the barrier to converting similar meaning text from one language to another.

A Better Language Translation took place when the size of these Transformer-based models increased and trained on large amounts of text data from different languages. Google Translate today uses Transformer based LLM trained in a wide range of different languages across the globe. This significant difference can be identified when using Google Translate to convert really different structured languages with similar meanings.

Chatbots and Virtual Assistants

One of the favorite applications of LLM. Building chatbots and virtual assistants are not a new technology. Even before these advancements, companies build chatbots using predefined output intents for business and customer interaction. These chatbots are not better for deep and good conversations with users.

The introduction of Large Language Models led to their widespread use in building chatbots and virtual assistants that can interact with humans in a meaningful way. At this time, everyone began to call "Highly Intelligent Chatbots" and LLMs became a synonym for chatbots even though they are not specifically designed for chatbots.

Today LLM based chatbots can converse with humans in a very intelligent and meaningful way, demonstrating their impressive ability to understand and generate human-like responses. These advanced chatbots leverage the vast amount of knowledge stored in Large Language Models to engage in natural and contextually relevant conversations. When fine-tuned LLMs on conversation datasets, it learns the pattern behind conversations, like how a conversation can be done, the common words used in conversation, the tone of the conversation, etc, and produces highly coherent text which is better for conversation.

Chatbots like Bard, ChatGPT, Bing Chat, etc are some of the examples of LLM-based chatbots. Virtual assistants like Siri, Alexa, Google Assistant, etc are the lightweight versions of these LLM-based chat models.

Text Generation & Summarization

Text Generation and Summarization are two essential applications of LLM. Text Generation involves producing highly coherent and quality text based on a user prompt. Many of the current State-of-the-art LLMs are highly capable of producing well-written articles, poems, essays, stories, novels, and even code which are all part of the Natural Langauge Generation (NLG).

Text generation requires two main stages, first, the LLM needs to understand the context of the given prompt by the user, known as Natural Langauge Understanding (NLU) as we discussed earlier, then it needs to calculate the probability distribution of the next word, all while maintaining creativity and diversity in the output.

Beyond standard text generation, it can perform Conditional Text Generation. Conditional text generation is a specialized form of text generation where LLM produces text based on specific conditions or constraints provided by the user. These conditions can take various forms, such as sentiment, style, content, or even a combination of multiple attributes.

For example, with conditional text generation, you can instruct the AI model to generate a poem with a romantic theme, an article written in a formal tone, or a story set in a futuristic world. It will then generate text that aligns with the given conditions. Conditional text generation allows for more personalized and customized content creation, giving users greater control over the type of text produced by LLM. 

In addition to text generation, LLMs excel in Summarization, which involves extracting the most relevant information from a piece of text and presenting it concisely. Summarization is invaluable in producing condensed and informative summaries for articles, documents, or lengthy texts, aiding users in quickly grasping essential information.

Automatic Speech Recognition (ASR) Systems

You might be wondering what the role of Langauge Models in speech-related tasks like Voice Recognition is, but deep inside voice recognition systems contain a Langauge Model for converting the acoustic features of the human voice into text.

Speech Recognition systems involve several steps. First, the audio signal is captured through a microphone or other recording devices, and the signal is preprocessed to remove background noise and enhance speech quality. Next, the acoustic model analyzes the acoustic features of the speech signal, such as phonemes, using statistical techniques like Hidden Markov Models (HMMs) or deep learning architectures like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs). This model converts the audio signals into the corresponding phonetic representations

After the acoustic model has done its job, Language Models enter the stage, Language Models build using RNNs or transformers are used for this job. However, today, Transformers are preferred over RNNs for building the Langauge Model part of ASR. The Langauge Model predicts the most probable words or sentences from a phonetic representation based on grammar, context, and language patterns. The final output is a meaningful text that corresponds to the spoken words.

One of the incredible ASR that can be found in open source is the one created by OpenAI known as Wispher. What sets Wispher apart is its ability to understand even whispered speech and convert it into text accurately. Even when speaking rapidly, Wispher can still recognize and transcribe the speech effectively. The ASR system is powered by a Large Language Model, making it highly flexible and capable of handling speech inputs at a higher level. If you want to witness Wispher in action, you can check out the demo on their website.

Question-Answering Systems

The idea of Question answering systems is straightforward, you just ask questions to a machine and it gives corresponding answers. Developing effective QA systems has been a long-standing challenge in artificial intelligence research. In recent years, large language models (LLMs) have led to major advances in QA capabilities.

LLMs are well-suited for QA tasks because of their ability to process contextual information and semantic meaning in text. QA often requires understanding the nuances of questions, passage context, and the relationships between them to produce the correct answer. The contextual learning ability of LLMs allows them to analyze these elements more effectively compared to previous rule-based or statistical QA systems.

One of the ways to improve LLMs' capability for QA is fine-tuning with QA datasets. Fine-tuning involves continuing the training of a pre-trained LLM on labeled QA examples. This adapts the model to the QA task by updating the internal parameters to predict answers from passages based on question context.

The performance of LLMs on QA benchmarks has improved steadily with scale. Models with billions of parameters, trained on large QA dataset text, achieve human-level performance across many QA tasks.

Sentiment Analysis & Market Research

Sentiment analysis is a process of analyzing texts like product reviews or social media posts to detect the attitudes and emotions behind them. Think of you as a company owner, using sentiment analysis, you can find whether your customers are satisfied with your product or not, are people saying positive or negative things about a product you launched a few days ago? This can give a quick sense of public opinion.

In the past, sentiment analysis systems relied on rules and dictionaries of positive/negative words. But language is complex! People express feelings in subtle, nuanced ways. Today LLMs can really help to do sentiment analysis at a higher level. As we said LLMs are great at Natural Langauge Understanding, because of this capability, they can understand any type of attitudes or emotions hidden behind textual information shared with the public as reviews or social media posts, as a result, you can do very detailed and informative market research on your product.

Another application that comes under sentiment analysis is fraud detection. Fraud detection is the process of identifying fraudulent activity online. For example, large-scale credit card companies could use sentiment analysis with LLMs to identify emails that are trying to scam customers into giving away their personal information.


Large Language Models (LLMs) have brought exciting possibilities to natural language processing. They can generate text, summarize content, translate languages, and answer questions. LLMs make content creation easier and improve human-computer interactions. As LLM technology advances, we can expect even more useful applications in the future, making AI language processing even better.