What is the Best Large Language Model (LLM)?

Large Language Models (LLMs) are artificial intelligence (AI) models that have been trained on vast amounts of text to understand and generate human-like language. These models are called “large” because they have an enormous number of training parameters (often billions or even trillions) that enable them to capture the intricacies of human language and perform a wide range of language-related tasks.

Perhaps the most popular use case for an LLM is ChatGPT, but it’s not the only one. In this article we’ll explain how LLMs work, what they can be used for and compare some of the best LLMs available today.

How do Large Language Models work?

Large Language Models (LLMs) are trained using a technique called unsupervised learning, which means they learn patterns and relationships in the text data without being programmed or given specific instructions. The training process involves feeding the model massive amounts of text data, such as books, articles, and websites, and allowing it to learn the statistical patterns and structures of the language.

During training, the model adjusts its parameters to minimize the difference between its predictions and the actual text data. This process is repeated many times, allowing the model to gradually improve its understanding of the language and its ability to generate coherent and contextually relevant text.

Training an LLM is notoriously expensive, as an example, Sam Altman says OpenAI spent over $100million training GPT-4, their latest and most advanced model.

What can Large Language Models be used for?

LLMs have a wide range of applications for businesses, researchers and even individuals. If you think about any time you have to brainstorm, write something, summarize text, extract insights and learnings, these tasks can all be done with an LLM.Some of the key use cases for LLMs include:

Content creation

One of the most popular applications of LLMs is text generation. Starting with a simple prompt, an LLM can generate coherent and contextually relevant text. This can be used for various purposes, such as writing articles, stories, or even code. You can even use it to generate ideas, plot outlines, character descriptions, and world building elements for novels, screenplays, and other creative projects.

Chatbots

Another popular use case for LLMs is chatbots. They’re used to engage in more natural conversations and provide users relevant replies to their questions. This can be used for building question-answer systems, tutoring bots, custom support chatbots and anything else where you’d need to retrieve specific and accurate information from a large knowledge base. Overall, they make it easier for users to find relevant information quickly.

Summarizing and research

LLMs can be used to parse and extract insights and information from large volumes of text data, such as scientific papers, legal documents, and financial reports. They can help identify patterns, summarize key points, and extract relevant information in a concise manner. This can save time and effort for users who need to quickly grasp the main ideas of a text without reading it in its entirety.

Coding

Some LLMs are trained on code repositories and can generate code snippets, autocomplete code, and even build simple applications based on natural language instructions. This can boost developer productivity.

Translating text to a different language

LLMs can also be used for language translation. By training on parallel text data (i.e., the same text in multiple languages), LLMs can learn to translate text from one language to another with high accuracy. Not all LLMs are capable of translating text, and some perform better than others at this task.

Classifying text

Text classification is another common use case for LLMs, where the model is trained to assign predefined categories or labels to text data. This can be used for tasks like spam detection, topic classification, or sentiment analysis for businesses looking to monitor customer feedback or analyze social media posts.

Proofreading and editing

LLMs can help identify and correct grammatical errors, typos, or stylistic inconsistencies in text, improving the overall quality of written content.

What is the best Large Language Model to use in 2024?

This is one of the most common questions we get, and is also the most difficult to answer. Firstly it depends on what you’re trying to achieve, different LLMs excel at different tasks. Secondly, the AI space is developing so fast that today’s best LLM could be tomorrow’s worst.

That said, we’ve covered the most popular LLMs available via PromptDrive in the table below. These can be used by most people to achieve what they want, but the best thing you can do is write a good prompt and test each LLM to see what gives you the best response today.

DeveloperModel Context lengthTraining data cut-offAccess via PromptDrive
OpenAIGPT-3.5 Turbo16k tokensSep 2021Yes
OpenAIGPT-4 Turbo128k tokensDec 2023Yes
AnthropicClaude 3 Haiku200k tokensAug 2023Yes
AnthropicClaude 3 Sonnet200k tokensAug 2023Yes
AnthropicClaude 3 Opus200k tokensAug 2023Yes
GoogleGemini Pro128k tokensEarly 2023Yes
GoogleGemini 1.5128k or 1m tokensEarly 2023Yes
Data accurate as of March 2024

What do tokens mean for Large Language Models?

Tokens are the fundamental units of text that an LLM processes and generates. It’s important to understand that tokens don’t necessarily equal words, they can represent words, part of a word, characters, or even bytes. The tokenization method depends on the specific LLM.

When you interact with an LLM, your input prompt is first tokenized into a sequence of input tokens. The model then processes these tokens to understand the context and generate a response. As the LLM generates a response, it produces a sequence of output tokens that form a coherent and relevant response to your input prompt. The model generates these tokens one at a time, based on its learned patterns and associations from its training data.

LLMs often have a maximum number of tokens they can process in a single input (context length) and generate in a single output (response length). These limits vary depending on the model, ranging from a few thousand to over a million tokens.

In simple terms, context length is one of the most important metrics for LLMs. The larger the context length, the more information you can send and receive to generate complete responses.

What to expect from Large Language Models in the future

While LLMs offer astonishing capabilities for understanding and generating human language, the field is rapidly evolving, with new models and techniques being developed constantly. In the future, we can expect LLMs to become even more powerful and versatile, with larger models and more sophisticated architectures.

As LLMs continue to advance, you will likely find them used in everything from healthcare, finance, education and entertainment. We believe that by learning how to use them today you’ll be more prepared for the future of work.

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