The context window of a large model is a key concept that affects the model's ability to process and generate text. The size of the context window determines the total number of input and output tokens that the model can consider in a single interaction.
Definition of Context Window
Context Window refers to the maximum number of tokens (tokens) that can be taken into account by the Large Language Model (LLM) when processing the input text and generating the output text at the same time. A token can be a word, a phrase or a punctuation mark. The size of the context window directly affects the depth of the model's understanding of the input information and the coherence of the generated content.
Input and output markers
- input marker: All textual content provided to the model by the user, including questions, instructions, etc.
- output marker: Model-generated responses or results.
At any given moment, the total number of input and output tokens cannot exceed the maximum length of the context window. For example, the maximum context window for the GPT-3.5-turbo model is 4096 tokens, which means that the sum of user input and model output cannot exceed this limit.
Input and output limitations of common large models
Different large language models have different context window restrictions. Here are some common models and their restrictions:
- GPT-3.5: The maximum context window is 4096 tokens.
- GPT-4: Larger context windows are supported, with specific values varying from version to version, usually between 8000 and 32000 tokens.
- Gemini 1.5: Maximum context window of up to 1 million markers.
- KIMI(large domestic model): up to 2 million markers.
These limitations affect not only the ability of the model to process information, but also the quality and coherence of the generated content.
Analysis of specific examples
Suppose we use GPT-3.5 for a task that requires it to summarize the contents of a book. The book has about 100,000 words, and GPT-3.5 can only handle 4096 tokens. If we break the content of the book into multiple segments with no more than 4096 tokens each, then we need to interact with the model step-by-step, inputting a portion of the content at a time and requesting a summary. This would allow the model to process the entire book, but it would increase complexity, as each call would need to ensure consistency between the previous and subsequent text.
Sample Scenarios
- user input: Please help me summarize the first chapter of this book (assuming the chapter is 3000 markers).
- model output: This summarizes the first chapter (assuming 500 markers were generated).
- The user continues to enter: Next, summarize Chapter 2 (again, 3000 markers).
In this case, the user needs to take into account that the previous information may be forgotten after each interaction, as the total input and output cannot exceed 4096 tokens. If the user refers to information from the first chapter in a subsequent request that is out of the context window, the model may not be able to respond accurately, thus affecting the consistency of the dialog.
summarize
Understanding the context window of a large model and its input and output constraints is critical to the effective use of these techniques. Properly utilizing these limitations can help developers design more efficient and coherent applications while also enhancing the user experience. In the future, as technology evolves, we can expect larger context windows, enabling large language models to handle more complex and long-form information.
