You've probably heard of Large language models (LLMs), the technology driving the AI revolution across the tech space and powering everything from Google's search results to OpenAI's ChatGPT.
These AI tools can process and generate massive amounts of text, blurring the lines between human and machine capabilities. From composing realistic dialogue to translating languages in real time, LLMs are finding applications across businesses and personal use.
LLMs are rapidly transforming the way we interact with technology, but with so many players on the market how do you decide which one to use?
How Do LLMs Work
LLMs are trained on enormous amounts of text data, often scraped from books, articles, code, and the internet. This exposure allows them to learn complex relationships between words and how language is used in different contexts.
They rely on artificial neural networks, a complex system loosely inspired by the human brain. These networks can identify patterns in the data and use them to process information and generate text. Many advanced LLMs utilize the Transformer architecture, which excels at understanding the relationships between words in a sentence and allows them to generate coherent and grammatically correct text.
Training a large language model is a complex task that requires significant computational resources and expertise. LLMs thrive on massive amounts of text data. The more data you have, the better the model will understand the nuances of language and generate human-quality text. Training data can be from books, articles, websites or code.
Data preparation means making the raw data suitable for the LLM to use. By carefully preparing the data, we equip the LLM with the knowledge base it needs to excel at tasks like text generation, translation, and question answering. This process includes;
- Cleaning and Filtering: Raw data often contains errors, inconsistencies, and irrelevant information. Tools are used to remove typos, grammatical mistakes, and HTML code remnants, ensuring the LLM learns from clean text.
- Normalization: The data might undergo normalization to ensure consistency. This could involve converting text to lowercase, stemming words (reducing them to their base form), or lemmatization (converting words to their dictionary form).
- Tokenization: Here, the text is broken down into smaller units that the LLM can understand and process. This can be words, characters, or even sub-word units. Tokenization allows the LLM to identify patterns and relationships between these units.
- Vocabulary Building: The unique tokens encountered during tokenization form the LLM's vocabulary. Depending on the model architecture, there might be a limit to the vocabulary size, requiring decisions about which words to include or exclude.
Types of LLM
There are two main types of LLM, autoregressive and conditional generative. Autoregressive models generate text word by word, predicting the next word based on the ones before. Conditional generative models consider additional information, like a specific prompt or desired writing style, to tailor their text generation.
Autoregressive Models
Autoregressive models are better at generating coherent and grammatically correct text. They work like a sophisticated autocomplete that predicts the next word based on the ones before. Autoregressive LLMs are trained on colossal amounts of text data which they then analyze to learn the probabilities of which word typically comes next in a sentence.
When a user inputs a prompt starting sentence the autoregressive model predicts the rest of the paragraph word by word. This chain of predictions allows for short form or long form content to be generated.
Given a prompt or starting sentence, the model predicts the most likely following word, then uses that prediction to inform the next word, and so on. This chaining of predictions allows them to generate paragraphs, articles, even entire scripts.
Autoregressive models are perfect for overcoming writer's block, rewriting content and creating summaries. They are used to power chatbots in natural language understanding and generation as well as translating text whilst maintaining fluency.
Conditional Generative Models
Unlike autoregressive models, conditional generative LLMs don’t operate word-by-word. They leverage additional information, known as ‘conditions’ that influence their text generation. These conditions could include starting topics or instructions of the model to follow, style indication such as professional essays or a casual email.
Conditional LLMs are more flexible than autoregressive models because they can consider the bigger picture. This allows for tailoring the output to match the condition, making it useful for creative content generation, with freedom to easily alter tone and format.
Best LLMs 2024
From streamlining workflows to unlocking creative possibilities, LLMs are revolutionizing human-computer interaction. But with a crowded market, choosing the right LLM for your needs can be a challenge - especially given the range of tools fighting for a space on the LLM leaderboard today.
To help you decide, we’re counting down ten of the best LLMs on the market in 2024, ranking each of them based on their features, popularity and performance.
Mistral AI
Mistral AI offers both open-source and commercial large language models. Mistral's commercial LLMs require access through Mistral's API, these LLMs named Mistral Small, Medium, and Large, offer improved performance and capabilities compared to their open-source counterparts. They also include features like function calling that allows the LLM to connect to external tools and guard railing which enforces specific policies within the model.
Mistral is known for its exceptional reasoning capabilities in multiple languages. This translates to a deeper understanding of context and logic, allowing it to tackle complex questions and generate a more comprehensive and informative response Mistral AI offers a range of open-source models with varying parameter sizes. This provides flexibility for researchers with different needs. Open-source means that the LLM’s code and training data are publicly available, and encourages collaboration within the research community. This approach is particularly beneficial for those who want to tailor the LLM for specific tasks or ensure responsible use.
Falcon LLM
Falcon LLM is a family of open-source large language models developed by the United Arab Emirates' Technology Innovation Institute (TII). It comes in various sizes, with Falcon 180B being the largest publicly available model. Falcon boasts impressive performance while requiring less training compute power compared to some competitors. This translates to lower operational costs for developers and researchers.
Falcon excels at text-based tasks like generation, machine translation, and question answering. It is also open-source, meaning it’s code and training data are readily available, allowing for transparency, collaboration, and further development by the research community.
Megatron-Turing NLG
Developed by NVIDIA, Megatron-Turing NLG stands out for its focus on efficiency and factual language generation. It prioritizes efficient use of resources during training and text generation, making it suited to cloud-based deployments or resource-constrained environments. It boasts a staggering 530 billion parameters, making it the largest monolithic transformer-based LLM available.
Megatron-Turing NLG excels at tasks requiring precise and reliable information. It can be a valuable tool for generating summaries of factual topics, writing informative reports, or answering questions in a clear and objective manner.
Bloom
Bloom is an open-source LLM from Big Science trained on a massive dataset of text and code. Being open source means that researchers can freely access, study, and even improve upon the model. Despite being open-source, Bloom demonstrates impressive performance in various benchmarks, particularly when fine-tuned for specific tasks.
Bloom can generate text in 59 languages, making it an incredibly valuable model for translation tasks and working with multi-lingual data. Bloom's combination of open-source nature, impressive multilingual capabilities, and strong performance makes it a compelling all rounder option.
Jurassic-1 Jumbo
Jurassic-1 Jumbo is a powerful LLM developed by AI21 Labs. It excels at complex text generation tasks including creative text formats like poems, scripts, musical pieces, or code. Its ability to analyze text can be valuable for tasks like sentiment analysis, summarizing large amounts of information, or identifying patterns in written content.
Unlike some LLMs, Jurassic-1 Jumbo isn't publicly available. Access is granted through AI21 Labs' platform, AI21 Studio. This allows for more controlled use and helps researchers explore its capabilities in a structured environment. Jurassic-1 Jumbo's combination of exceptional text generation, factual language understanding, and accessibility options makes it a versatile tool for various applications
Databricks DBRX
DBRX is a powerful LLM developed by Databricks. It stands out for its focus on efficiency and its close integration with the Databricks platform. It can generate different creative text formats like poems, code, and scripts as well as process information and answer questions with authority.
Its Mixture-of-Experts (MoE) technique helps DBRX achieve efficiency. This means the model consists of multiple "experts," each specializing in specific tasks. During generation, the most suitable expert is chosen for the job, reducing the computational resources needed compared to a single, massive model. The MoE architecture makes DBRX potentially well-suited for cloud deployments where computational resources might be limited.
Claude 3
Claude 3 is a family of large language models by Anthropic, a research company focused on developing safe and beneficial artificial intelligence. Claude 3 prioritizes safety and minimizing risks associated with LLMs, like bias, misinformation, or harmful outputs. Anthropic emphasizes responsible development practices to ensure Claude 3 is a trustworthy tool. Claude 3 Haiku is the fastest and most compact model in the LLM family, it excels at answering simple queries and requests quickly.
Read: Claude vs ChatGPT: Which is Better in 2024?
Claude 3 Sonnet balances performance and speed. It tackles complex tasks efficiently, making it suitable for large-scale AI deployments. Claude 3 Opus is the most intelligent model in the Claude 3 family and excels at navigating open-ended prompts and complex scenarios with fluency and understanding. It's particularly well-suited for demanding tasks that require in-depth analysis and creative thinking.
Llama 3
Meta’s Llama 3 LMM is trained on a massive dataset of text and code and not only boasts improved capabilities compared to previous models but is also integrated into Meta's social media platforms – Facebook, Instagram, and WhatsApp – as their new AI assistant, "Meta AI." It comes two different sizes, 8B and 70B parameters, that cater to different needs. The 8B version is efficient for personal use or experimentation, while the 70B version offers stronger performance for more demanding tasks.
Llama 3 is able to follow instructions and complete multi-step tasks. It can generate various creative text formats like poems, code, scripts, and more. Crucially, researchers can access and build upon Llama 3, fostering further AI development.
GPT-4o
OpenAI’s GPT-4o is a powerful LLM that powers ChatGPT Plus. Unlike its predecessors, GPT-4 can process both text and images. This allows it to analyze images, describe their content, and answer questions that rely on visual information.
Read: Meet GPT-4o: OpenAI’s Most Human AI Model Yet
GPT-4 can handle long sequences of text making it suited for tasks like long-form content creation, complex conversations, and document analysis. The LLM also performs well on various benchmarks, even achieving human-level scores on some professional exams.
Gemini Ultra
Google Gemini Ultra stands out as one of the best LLMs on the market today. Unlike other LLMs, Gemini is unique in that it was designed to be multimodal, meaning it could process multiple types of data simultaneously, including text, images, audio, video, and code.
Read: Google's Bard Has Just Become Gemini. What’s Different?
The Gemini ‘family’ is made up of three models: Gemini Ultra, for "highly complex tasks"; Gemini Pro, for "a wide range of tasks"; and Gemini Nano, for "on-device tasks". Each Gemini model is designed with advanced neural network architectures and focuses on providing nuanced and contextually aware responses. Its ability to understand and generate human-like language makes it versatile for applications like chatbots, content creation, and translation services.
