SLMs are often trained on smaller datasets compared to larger language models. This can result in:

1. Less generalization: SLMs may not generalize as well to new, unseen data, which can limit their performance on tasks that require a broad understanding of language.
2. Less robustness: SLMs may be more sensitive to noise, outliers, and other forms of data contamination, which can affect their performance on tasks that require robustness.

However, SLMs can still be trained on a wide range of tasks and domains, and the quality of the training data can have a significant impact on their performance.