Large language models are computer programs that can analyze and create text. They are trained using massive amounts of text data, which helps them become better at tasks like generating text. Language models are the foundation for many natural language processing (NLP) activities, like speech-to-text and sentiment analysis. These models can look at a text and predict the next word. Examples of LLMs include ChatGPT, LaMDA, PaLM, etc.
Parameters in LLMs help the model to understand relationships in the text, which helps them to predict the likelihood of word sequences. As the number of parameters increases, the ability of the model to capture complex relationships and its flexibility in handling rare words also increases.
ChatGPT is an open-source chatbot powered by the GPT-3 language model. It is capable of engaging in natural language conversations with users. ChatGPT is trained on a wide array of topics and can assist with various tasks like answering questions, providing information, and generating creative content.
It is designed to be friendly and helpful and can adapt to different conversational styles and contexts. With ChatGPT, one can have engaging and informative conversations on topics like the latest news, current events, hobbies, and personal interests.
GPT-3 vs. ChatGPT
- GPT-3 is a more general-purpose model that can be used for a wide range of language-related tasks.ChatGPT is designed specifically for conversational tasks.
- ChatGPT is trained on a smaller amount of data than GPT-3.
- GPT-3 is more powerful than ChatGPT, having 175B parameters, compared to ChatGPT, which has only 1.5B parameters.
Some AI tools that use the GPT-3 model:
Jasper is an AI platform that allows businesses to quickly create tailored content, blog posts, marketing copies, and AI-generated images. Jasper AI has been built on top of OpenAI’s GPT-3 model, and unlike ChatGPT, it is not free.
Writesonic is another model that uses the GPT-3 model. It can create quality content for social media and websites. Users can write SEO-optimized marketing copy for their blogs, essays, Google Ads, and sales emails to increase clicks, conversions, and sales.
Gupshup’s Auto Bot Builder is a tool that leverages the power of GPT-3 to automatically build advanced chatbots tailored to the needs of enterprises.
LaMDA is a family of Transformer-based models that is specialized for dialog. These models have up to 137B parameters and are trained on 1.56T words of public dialog data. LaMBDA can engage in free-flowing conversations on a wide array of topics. Unlike traditional chatbots, it is not limited to pre-defined paths and can adapt to the direction of the conversation.
Bard is a chatbot that uses machine learning and natural language processing to simulate conversations with humans and provide responses to questions. It is based on the LaMDA technology and has the potential to provide up-to-date information, unlike ChatGPT, which is based on data collected only up to 2021.
PaLM is a language model with 540B parameters that is capable of handling various tasks, including complex learning and reasoning. It can outperform state-of-the-art language models and humans in language and reasoning tests. The PaLM system uses a few-shot learning approach to generalize from small amounts of data, approximating how humans learn and apply knowledge to solve new problems.
Multilingual T5 (mT5) is a text-to-text transformer model consisting of 13B parameters. It is trained on the mC4 corpus, covering 101 languages like Amharic, Basque, Xhosa, Zulu, etc. mT5 is capable of achieving state-of-the-art performance on many cross-lingual NLP tasks.
DeepMind’s language model Gopher is significantly more accurate than existing large language models on tasks like answering questions about specialized subjects such as science and humanities and equal to them in other tasks like logical reasoning and mathematics. Gopher has 280B parameters that it can tune, making it larger than OpenAI’s GPT-3, which has 175 billion.
Chinchilla uses the same computing budget as Gopher, however, with only 70 billion parameters and four times more data. It outperforms models like Gopher, GPT-3, Jurassic-1, and Megatron-Turing NLG on many downstream evaluation tasks. It uses significantly less computing for fine-tuning and inference, greatly facilitating downstream usage.
Sparrow is a chatbot developed by DeepMind which has been designed to answer users’ questions correctly while reducing the risk of unsafe and inappropriate answers. The motivation behind Sparrow is to address the problem of language models producing incorrect, biased, or potentially harmful outputs. Sparrow is trained using human judgments to be more helpful, correct, and harmless than baseline pre-trained language models.
Claude is an Al-based conversational assistant powered by advanced natural language processing. Its goal is to be helpful, harmless, and honest. It has been trained using a technique called Constitutional Al. It was constrained and rewarded to exhibit the behaviors mentioned earlier during its training using model self-supervision and other Al safety methods.
Ernie 3.0 was released by Baidu and Peng Cheng Laboratory. It has 260B parameters and excels at natural language understanding and generation. It was trained on massive unstructured data and achieved state-of-the-art results in over 60 NLP tasks, including machine reading comprehension, text categorization, and semantic similarity. Additionally, Titan performs well in 30 few-shot and zero-shot benchmarks, showing its ability to generalize across various downstream tasks with a small quantity of labeled data.
Baidu, a Chinese technology company, announced that it would complete internal testing of its “Ernie Bot” project in March. Ernie Bot is an AI-powered language model similar to OpenAI’s ChatGPT, capable of language understanding, language generation, and text-to-image generation. The technology is part of a global race to develop generative artificial intelligence.
Huawei has developed a Chinese-language equivalent of OpenAI’s GPT-3 called PanGu-Alpha. This model is based on 1.1 TB of Chinese-language sources, including books, news, social media, and web pages, and contains over 200 billion parameters, 25 million more than GPT-3. PanGu-Alpha is highly efficient at completing various language tasks like text summarization, question answering, and dialogue generation.
OPT-IML is a pre-trained language model based on Meta’s OPT model and has 175 billion parameters. OPT-IML is fine-tuned for better performance on natural language tasks such as question answering, text summarization, and translation using about 2000 natural language tasks. It is more efficient in training, with a lower CO₂ footprint than OpenAI’s GPT-3.
BlenderBot 3 is a conversational agent that can interact with people and receive feedback on their responses to improve its conversational skills. BlenderBot 3 is built on Meta AI’s publicly available OPT-175B language model, which is approximately 58 times larger than its predecessor, BlenderBot 2. The model incorporates conversational skills like personality, empathy, and knowledge and can carry out meaningful conversations by utilizing long-term memory and searching the internet.
🔥 AI21 Labs
Jurassic-1 is a developer platform launched by AI21 Labs that provides state-of-the-art language models for building applications and services. It offers two models, including the Jumbo version, which is the largest and the most sophisticated language model ever released for general use. The models are highly versatile, capable of human-like text generation and solving complex tasks such as question answering and text classification.
🔥 LG AI Research
Exaone is AI technology that rapidly learns information from papers and patents and forms a database. It is an innovative breakthrough for tackling diseases through rapid learning of text, formulas, and images in papers and chemical formulas. The invention allows easier accumulation of human knowledge as data, easing the development of new drugs.
The Megatron-Turing Natural Language Generation (MT-NLG) model is a transformer-based language model with 530 billion parameters, making it the largest and most powerful of its kind. It outperforms prior state-of-the-art models in zero-, one-, and few-shot settings and demonstrates unparalleled accuracy in natural language tasks such as completion prediction, commonsense reasoning, reading comprehension, natural language inferences, and word sense disambiguation.