The article highlights that while LLMs have the potential to revolutionize how we interact with and use information, they are limited by the size of their context window. For instance, to ask questions about a novel not included in the model’s training data, the full text of the novel would need to be included in the prompt, which often exceeds the context window size. The limitations are due to memory constraints resulting from the architecture of LLMs. The next post promises to delve deeper into the embedding and tokenization process, memory constraints, and the nature of the token limit in LLMs.
Key takeaways:
- Large Language Models (LLMs) are a subset of language models that are trained on extremely large datasets, making them capable of accepting written English text prompts and outputting relevant results.
- The user input into an LLM prompt is called the context, which gets tokenized and embedded before being input into the LLM. The context window is the maximum amount of tokenized prompt text that can be input into the model in a single request.
- LLMs have the potential to revolutionize the way we interact with and use vast amounts of information, but their effectiveness is limited by the size of their context window and the data included in their training dataset.
- The limitations of the context window are due to memory constraints, which are a consequence of the architecture of the LLMs. The next post in the series will delve deeper into these memory constraints and the tokenization process.