The context window in a language model refers to the maximum number of tokens (words or word parts) the model can process in a single request, which includes both the input tokens (your prompt or the data you provide) and the output tokens (the response or generation produced by the model).
1. Input Tokens: These are the tokens or words in the prompt or data you give to the model. For instance, if you're feeding a paragraph to a model, the individual words, punctuation, and special characters will be tokenized into smaller units called tokens, and these will count toward the input tokens.
2. Output Tokens: These are the tokens or words generated by the model in response to your input. When you request the model to generate text, the number of tokens it produces also contributes to the total number of tokens.
Different LLMs have different restrictions on context window length.
1. GPT-3.5 (OpenAI): The context window is typically 4,096 tokens. This includes both input and output tokens. For example, if you provide 2,000 tokens in the input, the model will only generate up to 2,096 tokens in output before exceeding the limit. Refer: https://platform.openai.com/docs/guides/text-generation
2. GPT-4: The context window can go up to 8,192 tokens or 32,768 tokens for some variants, allowing for much longer input-output exchanges.
3. You can read the latest context window limits of GPT from here https://platform.openai.com/docs/models
4. Azure OpenAI GPT-4o preview support maximum input tokens 1,28, 000 and output tokens 32,768. Read more at the link https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models?tabs=python-secure%2Cglobal-standard%2Cstandard-chat-completions
5. Claude (Anthropic): Claude 1, 2, and 3 have context window limits around 9,000 tokens.
6. PaLM (Google): PaLM models, like PaLM-2, have context windows of up to 4,096 tokens.
7. LLaMA (Meta): LLaMA models have context windows up to 2,048 tokens for their smaller versions.
Why is the context window limited?
The limit on the context window exists due to both practical constraints and technical reasons.
1. Memory and Computation Constraints: Language models like GPT use transformer-based architectures, which are computationally expensive. Each token in the input affects the entire sequence, meaning the model must account for every token's interaction with others. As the context window grows, the computation grows exponentially. This requires more memory and processing power, making it inefficient to process long sequences without limits.
2. Efficiency: A limited context window allows the model to maintain efficiency in generating responses. With very large context windows, the model would need significantly more processing power, making it less accessible or slower.
3. Optimization for Practical Use: Most use cases do not require models to consider vast amounts of data at once. Limiting the context window helps maintain practicality and focus on the immediate input, ensuring responses are both relevant and timely.
4. Training and Architecture Constraints: During training, models are designed with specific context windows in mind. These limits are set based on the model architecture (e.g., GPT-3.5 has a context window of around 4,096 tokens). Larger context windows can result in more complex training and less manageable models.
References
https://learn.microsoft.com/en-us/answers/questions/1544401/why-models-maximum-context-length-is-4096-tokens-o Previous Next Home
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