8 best large language models for 2024

In recent years, the field of Natural Language Processing (NLP) has witnessed a remarkable surge in the development of large language models (LLMs). Due to advancements in deep learning and breakthroughs in transformers, LLMs have transformed many NLP applications, including chatbots and content creation.

Today, help you understand the LLMs influencing NLP today, we'll explore the top 8 LLMs influencing NLP, and how you can decide which one to work with:

• GPT-4

• Google Gemini

• Llama 3

• Claude 3

• Phi-2

• Mistral 8x22b

• Vicuna

• OLMo

But first, let's discuss large language models for the uninitiated.

What are large language models?

A large language model is a transformer-based model (a type of neural network) trained on vast amounts of textual data to understand and generate human-like language. LLMs can handle various NLP tasks, such as text generation, translation, summarization, sentiment analysis, etc. Some models go beyond text-to-text generation and can work with multimodalMulti-modal data contains multiple modalities including text, audio and images. data and tasks.

The transformer is a feed-forward network that takes its power from the self-attention mechanism. A basic transformer-based model consisting of an encoder and decoder is shown below:

In such a model, the encoder is responsible for processing the given input, and the decoder generates the desired output. Each encoder and decoder side consists of a stack of feed-forward neural networks. The multi-head self-attention helps the transformers retain the context and generate relevant output.

The "large" in "large language model" refers to the scale of data and parameters used for training. LLM training datasets contain billions of words and sentences from diverse sources. These models often have millions or billions of parameters, allowing them to capture complex linguistic patterns and relationships.

Training LLMs begins with gathering a diverse dataset from sources like books, articles, and websites, ensuring broad coverage of topics for better generalization. After preprocessing, an appropriate model like a transformer is chosen for its capability to process contextually longer texts. Training and fine-tuning follow afterward. This iterative process of data preparation, model training, and fine-tuning ensures LLMs achieve high performance across various natural language processing tasks.

Top 8 LLMs in 2024

Let’s explore these top 8 language models influencing NLP in 2024 one by one.

1. GPT-4

GPT-4 is a multimodal LLM developed by OpenAI. It takes images and text as input and produces multimodal output. It’s a powerful LLM trained on a vast and diverse dataset, allowing it to understand various topics, languages, and dialects. GPT-4 requires less detailed instructions compared to GPT-3. GPT-4 has 1 trillion,not publicly confirmed by Open AI while GPT-3 has 175 billion parameters, allowing it to handle more complex tasks and generate more sophisticated responses.

GPT-4 is prone to generating inaccurate information, a phenomenon often referred to as "hallucinating facts." For example, consider this mistake, where GPT responded with Elvis Costello when the answer was clearly Elvis Presley:

That said, hallucinations are not unique to GPT-4 alone, and it remains one of the most popular LLMs today.

To learn more about GPT and how to engineer prompts, check out this interactive course: Unleashing the Power of AI with OpenAI's GPT-3.

2. Gemini

Gemini is a multimodal LLM developed by Google and competes with others’ state-of-the-art performance in 30 out of 32 benchmarks. Its capabilities include image, audio, video, and text understanding. The Gemini family includes Ultra (175 billion parameters), Pro (50 billion parameters), and Nano (10 billion parameters) versions, catering various complex reasoning tasks to memory-constrained on-device use cases. Gemini can handle context windows of up to 32k tokens and is built using transformer architecture with a multi-query attentionMulti-query attention is an optimization in Transformer models that shares key and value projections across heads, improving efficiency while maintaining separate query projections. mechanism. They can process text input interleaved with audio and visual inputs and generate both text and image outputs.

Gemini performs better than GPT due to Google’s vast computational resources and data access. It also supports video input, whereas GPT's capabilities are limited to text, image, and audio.

Let's explore Gemini's impressive cross-modal reasoning capabilities. The illustration below shows a prompt containing a physics problem drawn by a teacher (left). The response (right) details the model's solution and explanation

As you can see, Gemini correctly analyzed the question, identified mistakes in the student's solution, and provided an explanation. Additionally, it responded to the task of using LaTeX for the mathematical components. (1)

3. Llama 3

Llama 3 was developed by Meta and built upon its predecessors, Llama 1 and 2. It is an open-source model that excels at contextual understanding, translation, and dialogue generation.

Llama 3 uses optimized transformer architecture with grouped query attentionGrouped query attention is an optimization of the attention mechanism in Transformer models. It combines aspects of multi-head attention and multi-query attention for improved efficiency.. It has a vocabulary of 128k tokens and is trained on sequences of 8k tokens. Llama 3 (70 billion parameters) outperforms Gemma Gemma is a family of lightweight, state-of-the-art open models developed using the same research and technology that created the Gemini models. and Mistral 7B instruct models at 5 LLM benchmarks.

Multimodal and multilingual capabilities are still in the development stage.

4. Claude 3

Claude 3 was developed by Anthropic and built upon its predecessors. It has three versions:

• Haiku (~20 billion parameters)

• Sonnet (~70 billion parameters)

• Opus (~2 trillion parameters).

So far, Claude Opus outperforms GPT-4 and other models in all of the LLM benchmarks.

These model variants follow a pay-per-use policy but are very powerful compared to others. Claude 3's capabilities include advanced reasoning, analysis, forecasting, data extraction, basic mathematics, content creation, code generation, and translation into non-English languages such as Spanish, Japanese, and French.

5. Phi-2

Phi-2, developed by Microsoft, has 2.7 billion parameters. Technically, it belongs to a class of small language models (SLMs), but its reasoning and language understanding capabilities outperform Mistral 7B, Llamas 2, and Gemini Nano 2 on various LLM benchmarks. However, because of its small size, Phi-2 can generate inaccurate code and contain societal biases.

Phi-2 is an open-source model as it recently acquired an MIT license.

The following illustration depicts Phi-2 accurately solving a similar problem to the physics problem we saw in the Gemini example.

6. Mixtral 8x22b

Mixtral 8x22b is a sparse mixture of experts model (SMoE) developed by Mistral AI. It is an open-source model that uses only 39B parameters out of 141B. Mixtral's capabilities include:

• Fluency in English, French, Italian, German, and Spanish

• Strong math and coding capabilities

• Native function calling

• 64K token context window for precise information recall from large documents

Compared to other models, Mixtral outperforms GPT 3.5 and Llama 2 70B. (2)

7. Vicuna

Vicuna is a chatbot fine-tuned on Meta's LlaMA model, designed to offer strong natural language processing capabilities. Its capabilities include natural language processing tasks, including text generation, summarization, question answering, and more.

Vicuna achieves about 90% of ChatGPT's quality, making it a competitive alternative. It is open-source, allowing the community to access, modify, and improve the model.

8. OLMo

The Allen Institute for AI (AI2) developed the Open Language Model (OLMo). The model's sole purpose was to provide complete access to data, training code, models, and evaluation code to collectively accelerate the study of language models.

OLMo is trained on the Dolma dataset developed by the same organization, which is also available for public use.

Which model to use?

Each model has its strengths and weaknesses, so the best choice will depend on factors like your specific application requirements, available resources, and priorities such as efficiency, multimodal capabilities, ethical considerations, or community collaboration.

Here's a comparison table to take into account:

We hope this overview helped you acquaint yourself with the LLM landscape.

If you're looking to dive deeper into the world of LLMs, Educative has several interactive courses you may find useful:

• Unleash the Power of Large Language Models Using LangChain

• Essentials of Large Language Models: A Beginner’s Journey

• Transformers for Natural Language Processing

We also offer Projects, which you can use to build as you learn (while growing your portfolio):

• Query CSV Files with Natural Language Using LangChain and Panel

• Build an LLM-Powered Wikipedia Chat Assistant with RAG

You can explore more of our hands-on AI resources here.