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- Large language models (LLMs) are machine learning systems that can process and generate natural language. They are trained by feeding them tons of data, which lets them learn how words work together1. They use a neural network architecture called transformer, which can handle long sequences of words and capture complex relationships1. They can also perform zero-shot learning, which means they can generalize to new tasks without additional training1. LLMs can be fine-tuned with supervised learning to improve their performance on specific tasks1. LLMs can be classified into generic, instruction-tuned, or dialog-tuned models, depending on their purpose and input2.Learn more:✕This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.
How large language models work
- Training LLMs using unsupervised learning LLMs must be trained by feeding them tons of data — a “corpus” — which lets them establish expert awareness of how words work together. ...
- Transformer processing ...
www.algolia.com/blog/ai/what-are-large-language-…There are three main kinds of large language models:
- Generic or raw language models predict the next word based on the language in the training data. These language models perform information retrieval tasks.
www.elastic.co/what-is/large-language-models - People also ask
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