Top 20+ LLM Use Cases & 4 Key Tips for Better Implementation

Large language models, or LLMs, including multimodal LLM, can help businesses improve their products and services by automating key processes. Nevertheless, picking the right one for your business can feel overwhelming with so many LLM use cases. This article will help you identify promising LLM use cases for your business, optimize their implementation, and maximize their impact to improve your offerings. Lamatic’s generative AI tech stack can ease this process by helping you quickly implement out-of-the-box LLM use cases that suit your goals.

What Is an LLM Model and Its Key Capabilities

Large language models are AI systems trained on massive datasets to help computers imitate human language. This leads to text generation, recognition, prediction, translation, and summarization capabilities. These deep learning models operate similarly to the human brain, processing information to recognize patterns better and make predictions.

Machine learning is impacting various industries, revolutionizing how people can use computers, enabling them to process massive amounts of data to learn to make decisions, and enhancing different types of applications. One technology making the most of deep learning, a specific type of machine learning, is large language models (LLMs).

LLMs can understand how sentences, characters, and words work together to perform tasks such as:

Translating text
Performing sentiment analysis
Generating responses

The Unique Power of Deep Learning

What differentiates deep learning models from standard machine learning models is that deep learning uses far more data points, relies on less human intervention to learn, and has a more complex infrastructure that requires greater computational power. Because of this, large language models are costly and aren’t as widespread as other machine learning models.

LLM Statistics: A Quick Overview

Large language models have been around since 2017. With each version, they became better at their tasks and delivered faster language processing. Starting with 2022, these models became more robust and accurate in their results, as shown by:

LLaMA
Bloom
GPT-3.5

Their popularity has caused a boom in the large language model market, which has led to the following results:

The global LLM market is projected to grow from $1,590 million in 2023 to $259,8 million in 2030, with a CAGR of 79,80% from 2023 to 2030.
The market will reach $105,545 million in North America by 2030, with a CAGR of 72,17%.
In 2023, the world’s top five LLM developers acquired around 88,22% of the market revenue.
By 2025, it's estimated that there will be 750 million apps using LLMs.
In 2025, 50% of digital work is estimated to be automated through apps using these language models.

How Do Large Language Models Work?

Here is a simplified step-by-step guide on how large language models (LLMs) work:

Training

Large language models (LLMs) must be trained using a large volume of data, also known as a corpus. This data comes from various sites on the internet, including GitHub and Wikipedia. Notably, the amount of data used to train an LLM varies depending on multiple factors, such as:

Model design
Type of data being used
Type of job the model needs to do
How well you want the model to perform

The data can be terabytes (TB) or petabytes (PB). LLM training usually involves multiple steps, including unsupervised and self-supervised learning approaches. During the unsupervised learning phase, LLMs are trained on unstructured and unlabeled data. This allows the models to derive relationships and correlations between words and concepts.

During the self-supervised learning (SSL) phase, a portion of the data is labeled to enable an LLM to identify different concepts accurately. This way, the model can quickly tell apart one part of the input from another.

Fine-Tuning

Once LLMs have been pre-trained, they’re fine-tuned to perform various tasks by training them on smaller task-specific datasets. The main idea behind fine-tuning a large language model is to improve its performance on specific tasks.

Deep Learning and Transformer Architecture

As an LLM goes through the transformer neural network architecture, it undergoes deep learning. This transformer architecture uses the self-attention mechanism to enable the LLM to understand and recognize the relationships and connections between words and concepts.

Practical Use

Once the LLM has been trained and fine-tuned, it can be used for practical purposes. Every time you query an LLM, it will generate a response, which can be an answer to your question, newly generated text, summarized text, or even a sentiment analysis report.

4 Major Challenges of LLM Use Cases and Ways to Avoid Them

1. Mitigating Biases Within Training Datasets

Large language models are trained on extensive data sets and adopt potential biases present within the sources. Failure to address these biases effectively can lead to increased biases within the models, consequently generating biased outputs and responses. Organizations must audit their training datasets for biases before model training begins to ensure smooth LLM operations. From there, data science engineers can implement strategies to minimize the impact of biases, such as:

Removing biased data
Augmenting datasets with diversity
Applying bias detection tools both pre- and post-LLM training

2. Improving Performance With New Data

Overfitting occurs when a model becomes finely tuned for the training data but doesn't perform well with new data. Data science engineers use regularization and early stopping during model training to mitigate overfitting.

Augmenting training data with diverse examples ensures that the model learns the patterns rather than memorizes specific instances. By addressing the overfitting issue, data science engineers can achieve high reliability for LLMs in real-world applications.

3. Protecting Data Security

A survey conducted in August 2023 by Datanami discovered that while 58% of companies work LLMs, they’re usually just experimenting. Only 23% of respondents planned to deploy commercial models or have already done so. Since data science engineers train LLMs using large datasets, they may include sensitive information. Engineers ensure the algorithms don't apply private data in an irrelevant context to avoid security breaches or unauthorized access.

4. Ensuring Compliance With Industry Standards

Companies working in demanding fields like healthcare, legal, and finance should verify that their LLM applications comply with industry regulations and standards. Ensuring regulatory compliance while leveraging LLMs is a challenging task that requires a prepared team.

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