18 of the best large language models in 2024

Introduction

The explosive growth of generative AI in 2023 will be driven by large language models. Still, they have been in existence for a long time.

LLMs are black box artificial intelligence systems that comprehend and produce new text by applying deep learning on incredibly large datasets. The attention mechanism, a machine learning technique modeled after human cognitive attention, was first presented in a research paper titled "Neural Machine Translation by Jointly Learning to Align and Translate" in 2014. This marked the beginning of the development of modern LLMs. The transformer model was introduced in a different paper titled "Attention Is All You Need" in 2017, which improved that attention mechanism.

The transformer model is the foundation for some of the most well-known language models used today, such as the bidirectional encoder representations from transformers (BERT) and the generative pre-trained transformer series of LLMs.

Just two months after its launch in 2022, ChatGPT-which utilizes a collection of language models from OpenAI-attracted more than 100 million users. Numerous rival models have been introduced since then. Some are open source, while others are owned by large companies like Microsoft and Google.

Staying up to date with the field's constant developments can be challenging. The most significant models from the past and present are listed here. It includes leaders who have had a major impact in the past as well as those who could do so in the future.

Top current LLMs

Here are a few of the most current and pertinent large language models. They process natural language and have an impact on future model architecture.

1. BERT
   Google unveiled the BERT family of LLMs in 2018. Data sequences can be changed into different data sequences using the transformer-based BERT model. BERT has 342 million parameters and is designed as a stack of transformer encoders. BERT was first trained on a sizable corpus of data, and it was then refined to carry out particular functions like sentence text similarity and natural language inference. In the 2019 version of Google search, it was utilized to enhance query comprehension.
2. Claude
   The focus of the Claude LLM is constitutional AI, which modifies AI outputs according to a set of principles to make the AI assistant it powers accurate, safe, and helpful. Anthropic was the company that created Claude. Claude 3.0 is the most recent version of the Claude LLM.
3. Cohere
   The enterprise AI platform Cohere offers multiple LLMs, such as Embed, Rerank, and Command. These LLMs can be tailored and trained to meet the unique needs of a particular business. One of the authors of Attention Is All You Need founded the company that produced the Cohere LLM. One of Cohere's advantages is that it is not limited to a single cloud, unlike OpenAI, which is restricted to Microsoft Azure.
4. Ernie
   The large language model Ernie from Baidu is what drives the Ernie 4.0 chatbot. The bot was launched in August 2023 and has over 45 million users. There are rumors that Ernie has 10 trillion parameters. Although capable in other languages, the bot performs best in Mandarin.
5. Falcon 40B
   The Technology Innovation Institute created the Transformer-based, Causal Decoder-Only model Falcon 40B. It was trained using English data and is publicly available. There are two more compact versions of the model that are offered: Falcon 1B and Falcon 7B (1 billion and 7 billion parameters). Falcon 40B is now available on Amazon SageMaker. You can get it for free on GitHub as well.
6. Gemini
   Google's Gemini LLM family is responsible for the company's Gemini chatbot. The model replaced Palm as the chatbot's power source, and it was rebranded from Bard to Gemini as a result of the switch. Because Gemini models are multimodal, they can process text as well as images, audio, and video. Additionally, a large number of Google products and apps incorporate Gemini. There are three sizes available: Ultra, Pro, and Nano. The three models are Ultra, which is the biggest and most powerful, Pro, which is a mid-tier model, and Nano, which is the smallest and most effective model for on-device tasks. In most benchmark evaluations, Gemini performs better than GPT-4.
7. Gemma
   The Gemma family of Google's open-source language models was trained using the same resources as Gemini. A 2 billion parameter model and a 7 billion parameter model are the two sizes available for Gemma. Gemma models are more efficient than similarly sized Llama 2 models on multiple evaluated benchmarks and can be run locally on a personal computer.
8. GPT-3
   In 2020, OpenAI released GPT-3, a large language model with over 175 billion parameters. A decoder-only transformer architecture is used by GPT-3. Microsoft declared in September 2022 that it was the only company using the underlying model of GPT-3. GPT-3 is ten times as large as its predecessor. The training data for GPT-3 comes from Wikipedia, Books1, Books2, WebText2, and Common Crawl.
   OpenAI released the parameter counts for GPT-3, the final model in the GPT series, to the general public. The paper "Improving Language Understanding by Generative Pre-Training" by OpenAI initially introduced the GPT series in 2018.
9. GPT-3.5
   GPT-3.5 is an improved version of GPT-3 that has fewer parameters. With the help of human feedback and reinforcement learning, GPT-3.5 was improved. The version of GPT that drives ChatGPT is GPT-3.5. OpenAI claims that the GPT-3.5 turbo is the most capable of the various models. Training data for GPT-3.5 is available through September 2021.
   It was also incorporated into the Bing search engine, but was later replaced by GPT-4.
10. GPT-4
    The largest model in OpenAI's GPT series, GPT-4, was made available in 2023. It is a transformer-based model, just like the others. Though there are rumors that the model contains more than 170 trillion parameters, its parameter count has not been made public like the others. GPT-4 is a multimodal model, according to OpenAI, which means it can process and generate images in addition to language instead of just language. Additionally, GPT-4 added a system message that allows users to customize the task and voice tone.
    GPT-4 performed on several academic exams at the level of a human. Some conjectured, upon the model's release, that GPT-4 approached artificial general intelligence (AGI), which denotes a level of intelligence comparable to or surpassing that of a human. Microsoft Bing search is powered by GPT-4, which is also available through ChatGPT Plus and will eventually be included into Microsoft Office products.
11. Lamda
    Announced in 2021, Google Brain developed a family of LLMs known as Lamda (Language Model for Dialogue Applications). Lamda was pre-trained on a sizable corpus of text and employed a decoder-only transformer language model. When former Google engineer Blake Lemoine went public in 2022 and asserted that the program was sentient, LaMDA attracted a lot of attention. It was developed using the Seq2Seq architecture.
12. Llama
    Extensive Linguistic Framework Released in 2023, Meta's LLM is called Meta AI (Llama). The largest version has a size of 65 billion parameters. Llama is currently open source, having previously only been available to approved researchers and developers. Smaller versions of Llama are available, requiring less processing power to use, test, and carry out experiments.
    GitHub, Wikipedia, CommonCrawl, and Project Gutenberg are just a few of the public data sources that Llama was trained on. Llama has a transformer architecture. Llama was effectively leaked and produced numerous descendants, including Vicuna and Orca.
13. Mistral
    Mistral is a 7 billion parameter language model that scores higher on every benchmark that is evaluated than Llama's language model of comparable size. Mistral also has a finely tuned model that is designed to follow instructions. Because of its smaller size, it can be hosted on its own and used effectively for business needs. A license based on Apache 2.0 was used for its release.
14. Orca
    Microsoft created Orca, a program with 13 billion parameters that is small enough to run on a laptop. By mimicking the reasoning processes attained by LLMs, it seeks to build upon the advances made by other open-source models. In many tasks, Orca performs on par with GPT-3.5 and can accomplish the same tasks as GPT-4 with a significant reduction in parameters. Orca is built on top of LLaMA's 13 billion parameter version.
15. Palm
    Google's 540 billion parameter transformer-based Pathways Language Model powers Bard, its AI chatbot. It was trained on several TPU 4 Pods, Google's proprietary machine learning hardware. Palm specialises in reasoning tasks like coding, classification, math and question answering. Palm is also very good at breaking down difficult tasks into easier subtasks.
    The name PaLM originates from a Google research project to develop Pathways, which eventually produced a single model that serves as the basis for numerous use cases. A number of tuned Palm versions exist, such as Sec-Palm for cybersecurity deployments that speed up threat analysis and Med-Palm 2 for life sciences and medical data.
16. Phi-1
    Microsoft Phi-1 is a transformer-based language model. Phi-1 was trained on a set of textbook-quality data for four days, with just 1.3 billion parameters. A trend toward smaller models trained on higher quality and synthetic data is exemplified by Phi-1.
    Due to its smaller size, Phi-1 has fewer general capabilities and is primarily focused on Python coding.
17. StableLM
    The open-source language models known as StableLM were created by Stability AI, the same company that created the image generator known as Stable Diffusion. As of the time of writing, there were 3 billion and 7 billion parameter models available, and 15 billion, 30 billion, 65 billion, and 175 billion parameter models are under development. StableLM strives to be open, friendly, and helpful.
18. Vicuna 33B
    Another well-known open-source LLM that was developed from Llama is Vicuna. It was created by LMSYS and fine-tuned with the help of information from sharegpt.com. Several benchmarks show that it is smaller and less capable than the GPT-4, but it still performs admirably for a model of its size. GPT-4 has trillions of parameters, while Vicuna only has 33 billion.
19. LLM precursors
    Although LLMs are a relatively new phenomenon, their precursors date back decades. Learn how the development of modern LLMs was facilitated by distant precursor ELIZA and recent precursor Seq2Seq.
20. Seq2Seq
    A deep learning technique called Seq2Seq is used for natural language processing, image captioning, and machine translation. It is a Google invention and the foundation of several of their current LLMs, such as LaMDA. AlexaTM 20B, Amazon's large language model, is also based on Seq2Seq. It combines the use of decoders and encoders.
21. Eliza
    Created in 1966, Eliza was one of the first programs for natural language processing. It is one of the earliest instances of a language model. Eliza used pattern matching and substitution to mimic a conversation. Running a specific script, Eliza could use weights to indicate which keywords to respond to in order to parody the interaction between a patient and therapist. Joshua Weizenbaum, the man behind Eliza, wrote a book about the limits of computation and artificial intelligence.

How Large Language Models Operate

Large language models operate by consuming massive amounts of information in the form of written text, such as books, articles, and internet data. These deep learning models get better at understanding and using human language as they process more high-quality data.

Let's take a closer look at the fundamental idea behind how they work:

1. Architecture
   Large language models are based on an innovative transformer model architecture. With the help of this deep learning technique, the LLM can handle long-range dependencies between words by using the attention mechanism to evaluate the importance of each word in a sequence.
2. Attention Mechanism
   The attention mechanism, which enables the model to concentrate on various portions of the original input text when producing output, is one of the essential elements of the transformer architecture.
   This allows it to capture relationships between words or subwords in the text, regardless of their distance in text
3. Training Data
   Massive data sets that include portions of the internet are used to train LLMs. This helps them learn style, rhetoric, reasoning, and even a semblance of common sense in addition to grammar and facts.
4. Tokens
   Tokens, which can be as small as a single character or as large as a word, are used to divide up text. The model generates and understands language as it processes these tokens in batches.
5. Training Process
   Pre-training entails unsupervised learning on large text corpora. They predict the next word in a sequence while also learning language patterns, facts, and even some reasoning abilities.
   Fine-tuning: Following pre-training, models are refined using labeled data on particular tasks (such as translation and summarization). The model is tuned through this process of instruction tuning to make it more effective at those tasks.
6. Layered Approach
   Recurrent neural networks and attention mechanisms are found in each of the transformer architecture's several layers. Information is abstracted more and more as it moves through these layers, enabling the model to produce text that is coherent and appropriate for the context.
7. Generative Capability
   LLm's are generative, which means they can produce coherent text based on user inputs. A large language model is capable of generatively producing language through patterns learned from the attention mechanism.
8. Interactivity
   Large language models can respond to queries, generate text based on prompts, and even emulate specific writing styles in real-time through a chatbot model.
9. Limitations
   LLMs do not truly "understand" text. From their training set of data, they identify patterns.

Because they are sequence-sensitive, they may respond to questions that are slightly different in a different way.

They do not have the same ability to reason or think critically as humans. Their responses are predicated on patterns observed during training.