What Are Some Real Examples of Large Language Models, and How Are They Used?

Large Language Models (LLMs) are used in Chatgpt, Gemini, and more, enhancing their ability to interact and perform tasks for their users while having human-like conversations. AI tools are powered by LLMs that boost their speed and make them efficient for the users. Do you want to know what are some real examples of Large Language models that are applied in all of these sectors? This article will guide you to numerous facts about these models, including their uses, pros, and cons. 

What is a Large Language Model (LLM)?

LLMs, or large language models, are AI tools that are trained on an excessive amount of datasets of text content, which helps these models to understand humanised tone, accent, pattern, or grammar. This enables this LLM to perform tasks and actions based on human input in the form of a prompt to generate output like writing, answering queries or questions, translating languages, and even generating code. 

The factor that sets it apart from traditional tools is that LLMs are designed to learn and adapt to the intricacies of data. People generally use generative AI tools, which are based on LLMS, to generate output like content, images, or code based on prompts.

What Are the Most Well-Known Examples of Large Language Models?

Almost all the AIs that you are using include large language models. Some of the well-known examples include

1. Gemini by Google—Google has powered Gemini into almost all the latest Android devices, and it is compatible with Google Workspace and Bard chatbots. 
2. Meta’s LLaMA – This is an open-source model that is available for both types of uses, like academic or personal use. This includes natural language processing, conversational text, and generative AI.
3. OpenAI’s ChatGPT – This is one of the pioneer and most used models that powers AI tools like ChatGPT and Microsoft Copilot. These tools are designed in a way to respond in a humanized tone.
4. Claude by Anthropic – This LLM possesses similar capabilities to GPT, which is used to generate text, images, or videos. This AI model includes multiple LLMs that enhance its ability in natural language processing (NLP).
5. Cohere’s Command R+ – This model is specifically used to follow instructions that are based on language tasks.

All these AIs consist of diverse features such as multilingual performance, training data, size, and NLP. These tools can be used for commercial purposes, and some also include the best large-language models that are free to use.

Where Are These LLMs Being Used in Real Life?

In short, almost everywhere. Multiple industries are automating their tasks with the help of AI that is designed with LLMs. Here is a curated list of large language model examples in AI that are being used nowadays.

1. Sales & Marketing– Marketing Companies use LLMs to power chatbots that can generate content that is used on their social media to attract customers. It includes tools like Jasper and copy.ai that are created for copywriting.
2. Coding Assistants – Developers use AI while working on frontend and backend development tasks. They use GitHub Copilot, which can help them in fixing bugs and writing full-fledged code for software development.
3. Healthcare – The healthcare sector is being automated with innovation in Industry 5.0, which also includes using AI tools to generate medical records, assist in diagnosis, and perform pre-surgical operations. 
4. Customer Service – This sector includes repetitive tasks and constant interaction with consumers. Companies opt for creative AI tools, which include chatbots that handle customer queries. These tools resolve these issues and provide information through humanised interactions.

How Do Different Large-Length Models Compare?

You can understand multiple LLMs by analysing their features to know your options.

Google’s Gemini

It is introduced by Google, which includes multilingual tasks and its integration with Google Workspace and applications like Gmail, Docs, and Google Sheets.

Mistral/Mixtral

These are one of those models that can work on modest hardware setups as well. They are known for their efficient performance and good quality, which satisfies their users. 

OpenAI’s GPT

Series Chat GPT models are the most used LLMs that are included in GPT-3.0, GPT-4.0, and previous models as well. This model is best rated for its fluency, diversity, and integration with Microsoft tools. GPT-4 is mostly used for content creation and reasoning ability. 

Meta’s LLaMA

It is backed up by its open-source alternative and a strong academic background that helps it to perform better in comparison to other models. It is a great option for working on projects with transparency.

Also Read: What Are Vision-Language Models?

What Are the Limitations of Today’s LLMs?

Every tool comes with limitations. LLMs can have some constraints as well, which include

1. False Information—Data generated by LLMs can not be accepted as facts, as they are trained on limited datasets, so there is a chance of incorrect information.
2. Biasness—Due to inefficient data training, LLMs can include bias in their reasoning and data. 
3. Privacy Concerns – Some models include data storage in the cloud, prompts, and outputs that can contain sensitive information. 
4. Energy Consumption – LLMs use a massive amount of energy while training on data and running AI tools as well. 
5. Cost Constraints – LLMs work with the integration of APIs, which can be expensive as they require high-performance models to work on. 

These limitations can be mitigated when developers invest in responsible AI so that LLMs can become more efficient and safe to use. 

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