Top Large Language Model Examples in 2024
Large language models (LLMs) have taken over the internet. In January 2023, OpenAI’s ChatGPT had 100 million monthly active users, setting the record for the fastest-growing user base ever. The demand for LLMs is high because there are many use cases, such as:
- Text generation
- Sentiment analysis
- Generating valuable insights from unstructured data
- Content creation
- Reading comprehension, summarization, classification
- Machine translation
- Question answering
Large language models are continuously improving through training on more data and improvements in the deep learning neural networks that enable them to understand language.
As a new technology, large language models are still in the early stages of being used in business. Business leaders who might be unaware of the leading large language model examples can read this article to catch up on large language models.
What are large language models, and how do they work?
Large language models are deep learning neural networks that can understand, process, and produce human language by being trained on massive amounts of text. LLMs can be categorized under natural language processing (NLP), a domain of artificial intelligence aimed at understanding, interpreting, and generating natural language.
During training, LLMs are fed data (billions of words) to learn patterns and relationships within the language. The language model aims to figure out how likely the next word will be based on the words that came before it. The model takes in a prompt and uses the probabilities (parameters) it learned during training to generate a response.
If you are new to large language models, check our “Large Language Models: Complete Guide in 2024″ article.
How are large language models trained?
Large language models such as ChatGPT are trained using a process called supervised learning. During training,
- First, a large set of text inputs and their corresponding outputs are given to the model to predict the output given a new input.
- The model uses an optimization algorithm to adjust its parameters to minimize the difference between its predictions and the actual outputs.
- Then, the training data is given to the model in small batches.
- The model makes predictions for each batch and changes its parameters based on the errors it sees.
- This process is repeated several times, allowing the model to gradually learn the relationships and patterns in the data.
Check out our article on large language model training to learn more on this subject.
Examples of large language models
We present the leading large language models in the table below with parameters suited for enterprise adoption. We provided some additional information on the most impactful models.
Model | Developer | Launch Year | Number of Parameters | Number of Languages Covered | Open Source | On-prem/Private Cloud | Research/Paper |
---|---|---|---|---|---|---|---|
GPT-3 | OpenAI | 2020 | 175 billion | +95 natural languages + 12 code languages | No | No Only through Microsoft Azure | https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf |
BERT | 2018 | 340 million | 104 languages in multilingual model | Yes | Yes | https://arxiv.org/abs/1810.04805 | |
BLOOM | BigScience | 2022 | 176 billion | 46 natural languages 13 code languages | Yes | Yes | https://huggingface.co/bigscience/bloom |
NeMo LLM | NVIDIA | 2022 | 530 billion | English only | Yes | Yes | https://www.nvidia.com/en-us/gpu-cloud/nemo-llm-service/ |
Turing NLG | Microsoft | 2020 | 17 billion | English only | Yes | No | https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/ |
XLM-RoBERTa | Meta | 2020 | 354 million | 100 natural languages | Yes | Yes | https://arxiv.org/abs/1911.02116 |
XLNet | Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov,
Quoc V. Le
| 2020 | 340 million | English only | Yes | Yes | https://arxiv.org/abs/1906.08237 |
OPT | Meta | 2022 | 175 billion | English only | Yes | Yes | https://arxiv.org/abs/2205.01068 |
LaMDA | 2021 | 137 billion | English only | Yes | No | https://blog.google/technology/ai/lamda/ | |
Classify, Generate, Embed | Cohere | 2021 | NA | +100 natural languages | Yes | Yes | https://docs.cohere.ai/docs/the-cohere-platform |
Luminous | Aleph Alpha | 2022 | NA | English, German, French, Italian and Spanish | No | Yes | https://www.aleph-alpha.com/luminous |
GLM-130B | Tsinghua University | 2022 | 130 billion | English & Chineese | Yes | Yes | https://keg.cs.tsinghua.edu.cn/glm-130b/posts/glm-130b/#fnref:5 |
CPM-2 | Beijing Academy of Artificial Intelligence &Tsinghua University | 2021 | 11 billion | English & Chineese | Yes | Yes | https://arxiv.org/pdf/2106.10715.pdf |
ERNIE 3.0 | Baidu | 2021 | 10 billion | English & Chineese | Yes | Yes | https://arxiv.org/abs/2107.02137 |
Note: Features such as the number of parameters and supported languages can change depending on the version of the language model.
1- BERT
Bidirectional Encoder Representations from Transformers, or BERT for short, is a large language model released by Google in 2018. BERT utilizes the Transformer Neural Network architecture, which was introduced by Google in 2017.
Until the introduction of BERT, the most common application for NLP was recurrent neural networks (RNNs), which looked at input text as left-to-right or combined left-to-right and right-to-left. Unlike old one-directional models, BERT was trained bidirectionally, which enables it to have a deeper sense of language context and flow.
2- GPT-3
GPT-3 is the latest Generative Pre-Trained (GPT) model from Open AI, released in 2020. GPT-3 is also based on the Transformer architecture, and it is pre-trained in an unsupervised manner, making it applicable to many use cases by fine-tuning with zero, one, or few-shot learning techniques.
3- BLOOM
An initiative by BigScience, BLOOM is a multilingual language model among the largest open-source models. BLOOM also has a Transformer-based architecture, the most popular choice among modern language models.
If you want to learn more about large language models, don’t hesitate to contact us:
This article was drafted by former AIMultiple industry analyst Berke Can Agagündüz.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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