Word embeddings do benefit from large training datasets. However, it is not an absolute requirement as word embeddings can still be trained effectively on smaller datasets, although the quality may be affected.

  Word embeddings are learned by utilizing the distributional hypothesis, which assumes that words occurring in similar contexts have similar meanings. To capture these contextual relationships, word embeddings algorithms are trained on large amounts of text data.

  The amount of training data directly impacts the coverage and quality of the learned word embeddings. Having a large training dataset allows for a more comprehensive exploration of word co-occurrence patterns and a better estimation of word relationships.

  With a larger training dataset, the word embeddings model has a higher probability of encountering different contexts in which words appear, resulting in more accurate embeddings. It enables the model to capture nuanced relationships between words and acquire a better understanding of word meanings.

  However, it is worth noting that training on large datasets can also introduce noise and unnecessary complexity. In some cases, smaller datasets may be sufficient for specific tasks or domains, especially when the focus is on a specialized vocabulary.

  When training word embeddings on smaller datasets, it becomes more crucial to ensure that the data is representative of the target domain. Additionally, techniques like transfer learning or starting with pre-trained word embeddings on larger datasets can be employed to leverage the knowledge learned from the larger dataset. These approaches can help overcome limitations posed by smaller training datasets.

  In summary, while larger training datasets are generally beneficial for word embedding training, it is possible to train effective word embeddings on smaller datasets, with the understanding that the quality and coverage may be compromised to some extent.