Small language models are characterized by:

Small language models are indeed characterized by a smaller training dataset. This characteristic is crucial as it determines how the model learns patterns, nuances, and structures within the language as it is being trained. A smaller training dataset often leads to simpler models that might not capture as much complexity as their larger counterparts, which require extensive datasets to identify patterns across varied contexts and intricate language usage.

In contrast, larger language models typically have a greater number of parameters, which allow them to encode and process vast amounts of data more effectively. Enhanced memory capabilities generally refer to larger models that can maintain context or information over longer interactions, not smaller ones. Furthermore, small language models typically do not require extensive computational resources, contrasting with larger models that necessitate significant computational power for both training and inference due to their complexity and size. Therefore, recognizing the relationship between the size of a model and its training dataset is key in understanding the distinction between small and large language models.