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Forget 32K of GPT4: LongNet Has a Billion Token Context
Summary
Microsoft recently published a paper proposing a transformer model that can scale up to a billion tokens, solving the major obstacle of practical use for large language models. This article explains how large language models work, their current networks, the difficulty of scaling, Microsoft's solution (LongNet), the distributed trainer, results and verification of scaling to 1 billion tokens, and closing thoughts.
Q&As
What is the proposition Microsoft has put forward concerning language models?
Microsoft has proposed and developed a transformer model that can scale to theoretically a billion tokens.
What is the purpose of the transformer model in the development of large language models?
The purpose of the transformer model is to create a map of the language where given the input text, an output is generated based on that map.
What is the current obstacle in practical use cases for large language models?
The current obstacle in practical use cases for large language models is the context length restriction.
What is Microsoft's solution to scaling LLMs?
Microsoft's solution to scaling LLMs is LongNet.
What are the results and verification of scaling to 1 billion tokens?
The results and verification of scaling to 1 billion tokens include the successful training of LongNet on a distributed system and the successful generation of text with a context length of up to 1 billion tokens.
AI Comments
👍 This article provides an excellent overview of the potential of LongNet, Microsoft's latest innovation in large language models, and its ability to provide a practically unlimited context length.
👎 The article does not provide enough technical details to help readers understand the complexities of LongNet's architecture.
AI Discussion
Me: It's about Microsoft's new LongNet model, which is a transformer model that can scale to a billion tokens, removing the major obstacle in the practical use case for large language models.
Friend: Wow, that's impressive! What are the implications of this?
Me: Well, having a larger context length in language models means that they can better understand different contexts and nuances of the same words, leading to more accurate predictions. In addition, this could potentially be used to create more powerful AI chatbots that can better understand natural language and provide more accurate responses. Finally, it could lead to more accurate natural language processing applications, such as automated translation services.
Action items
- Research and explore the implications of LongNet and its potential applications.
- Experiment with LongNet and other large language models to understand their capabilities and limitations.
- Develop a project to apply LongNet to a specific use case and evaluate its performance.
Technical terms
- GPT-3 and GPT-4
- GPT-3 and GPT-4 are large language models developed by OpenAI. They are used to generate text based on a given input.
- Context Length Restriction
- This is the limitation of the number of tokens that can be used in a language model. GPT-3 and GPT-4 are limited to 2048, 4096, and 32768 tokens.
- Large Language Models (LLMs)
- LLMs are deep learning models that have millions or billions of parameters. They are trained on large corpora of text from the internet.
- Self-Attention
- Self-attention is a mechanism used in language models to determine the relationship between words in a given string. It is used to predict the next token in the sequence.
- Token
- A token is a word or part of a word that is used as a functional unit of a sentence.