The capacity for input/output examples in models like Vertex AI Language isn't explicitly limited by a fixed number. However, several factors can influence the effectiveness of using a large number of examples.
Larger models often have higher capacity to process larger volumes of examples due to their increased parameters and computational resources. Still, there might be practical limits based on the specific model size and available resources. Many language models, including those in Vertex AI, have token limits for input sequences. If your examples are lengthy (1k tokens each), you might encounter constraints in fitting these examples into the model’s token limits or memory constraints.
Processing a large number of examples simultaneously might impact the model’s efficiency and processing speed. The more examples provided, the longer it might take for the model to process and learn from them. And providing too many examples could potentially lead to overfitting on the training data, hindering the model's ability to generalize well on new, unseen data.
It's essential to strike a balance between providing sufficient diverse examples for the model to learn from and overwhelming it with an excessive amount of information that could hinder its learning and generalization capabilities. If you plan to use a significant number of examples, consider experimenting with smaller subsets initially to gauge the model's performance and determine an optimal quantity that doesn't compromise its effectiveness. Additionally, pre-processing or summarizing the examples might help reduce their token count while retaining essential information.
You can also visit the quotas and limits documentation of Vertex AI.
LinkedIn
Twitter
