What is the large language model (LLM) that makes ChatGPT explode
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AI applications are summarizing articles, writing stories, and holding long conversations—and large language models are doing the heavy lifting.
A Large Language Model, or LLM, is a deep learning algorithm that can recognize, summarize, translate, predict, and generate text and other content based on knowledge gained from massive datasets.
Large language models are one of the most successful applications of Transformer models. They are used not only to teach AI human language, but also to understand proteins, write software code, and more.
In addition to accelerating natural language processing applications -- such as translation, chatbots, and AI assistants -- large language models are used in use cases in healthcare, software development, and many other fields.
What are large language models good for?
Language is not just for human communication.
Code is the language of computers. Protein and molecular sequences are the language of biology. Large language models can be applied to languages or scenarios that require different types of communication.
These models broaden the range of applications of AI across industries and businesses, and promise to unleash a new wave of research, creativity, and productivity as they help generate complex solutions to the world's toughest problems.
For example, AI systems using large language models can learn from databases of molecular and protein structures, and then use that knowledge to provide actionable compounds that could help scientists develop breakthrough vaccines or treatments.
Large language models are also helping to create reimagined search engines, tutoring chatbots, authoring tools for songs, poems, stories and marketing materials, and more.
How do large language models work?
Large language models learn from large amounts of data. As the name suggests, the heart of LLM is the size of the dataset it is trained on. But as artificial intelligence develops, so does the definition of "big."
Now, large language models are usually trained on datasets large enough to contain almost everything written on the internet over a long period of time.
Such large amounts of text are fed into AI algorithms using unsupervised learning -- when a model is given a dataset without clear instructions on what to do with it. With this approach, large language models can learn words, the relationships between them and the concepts behind them. For example, it can learn to distinguish between the two meanings of the word "bark" based on context.
Just as a human master of a language can guess what will come next in a sentence or paragraph—or even come up with new words or concepts themselves—large language models can apply their knowledge to predict and generate content.
Large language models can also be tailored for specific use cases, including through techniques such as fine-tuning or hint tuning, which is the process of giving a model a small amount of data to focus on in order to train it for a specific application.
Due to its computational efficiency in processing sequences in parallel, the transformer model architecture is the building block behind the largest and most powerful LLMs.
Popular Applications for Large Language Models
Large language models are opening up new possibilities in areas such as search engines, natural language processing, healthcare, robotics, and code generation.
The popular ChatGPT AI chatbot is an application of large language models. It can be used for countless natural language processing tasks.
Nearly limitless applications of the LLM also include:
- Retailers and other service providers can use large language models to deliver better customer experiences through dynamic chatbots, AI assistants, and more.
- Search engines can use large language models to provide more direct, human-like answers.
- Life science researchers can train large language models to understand proteins, molecules, DNA, and RNA.
- Developers can use large language models to write software and teach robots to perform physical tasks.
- Marketers can train a large language model to organize customer feedback and requests into clusters, or to categorize products based on their descriptions.
- Financial advisors can use large language models to summarize earnings calls and create transcripts of important meetings. Credit card companies can use LLM for anomaly detection and fraud analysis to protect consumers.
- Legal teams can use large language models to help with legal interpretation and transcription.
Running these large models efficiently in production is resource-intensive and requires expertise, so enterprises turn to NVIDIA Triton Inference Server, software that helps standardize model deployment and deliver fast and scalable AI in production.
Where to find large language models
In June 2020, OpenAI released GPT-3 as a service powered by a 175 billion parameter model that can generate text and code with short written prompts.
In 2021, NVIDIA and Microsoft developed the Megatron-Turing Natural Language Generation 530B, one of the world's largest reading comprehension and natural language inference models, which simplifies tasks such as summarization and content generation.
HuggingFace last year launched BLOOM, an open large-scale language model capable of generating text in 46 natural languages and a dozen programming languages.
Another LLM, Codex, converts text into code for software engineers and other developers.
NVIDIA provides several tools to simplify the construction and deployment of large language models:
- The NVIDIA NeMo LLM service provides a fast path to customizing large language models and deploying them at scale using NVIDIA's managed cloud API or via private and public clouds.
- NVIDIA NeMo Megatron , part of the NVIDIA AI Platform, is a framework for training and deploying large language models simply, efficiently, and cost-effectively. Designed for enterprise application development, NeMo Megatron provides an end-to-end workflow for automating distributed data processing; training large-scale, custom model types, including GPT-3 and T5; and deploying these models for large-scale inference.
- NVIDIA BioNeMo is a domain-specific hosting service and framework for large language models in proteomics, small molecules, DNA, and RNA. It is built on the NVIDIA NeMo Megatron for training and deploying large biomolecular transformer AI models at supercomputing scale.
The Challenge of Large Language Models
Scaling and maintaining large language models can be difficult and expensive.
Building a fundamentally large language model typically requires months of training time and millions of dollars.
And because LLM requires large amounts of training data, developers and businesses will find it challenging to access sufficiently large datasets.
Due to the scale of large language models, deploying them requires technical expertise, including a deep understanding of deep learning, transformer models, and distributed software and hardware.
Leaders in many technology domains are working to advance the development and build resources to expand access to large language models to benefit consumers and businesses of all sizes.
More exciting content:
https://www.nvidia.cn/gtc-global/?ncid=ref-dev-876561