Using pre-trained contextualized embeddings has its own limitations. Here are some of them:

  1. Lack of domain-specific knowledge: Pre-trained embeddings are trained on large-scale publicly available corpora, which may not capture the specific vocabulary and semantics of a particular domain. This can lead to less accurate representation of domain-specific terms and concepts.

  2. Inability to handle out-of-vocabulary words: Pre-trained embeddings are trained on a fixed vocabulary, and they might not have representations for rare or out-of-vocabulary words. This can result in missing or unreliable embeddings for such words, leading to degraded performance in downstream tasks.

  3. Fixed contextualization: Pre-trained contextualized embeddings are typically generated by fixed-length neural network models like transformers. These models have a limited context window, meaning they cannot fully capture long-range dependencies in the text. Consequently, they may not perform well in tasks that require extensive context understanding beyond the context window.

  4. Limited transferability across tasks: While pre-trained embeddings can be fine-tuned for specific tasks, their transferability across diverse tasks is not guaranteed. The embeddings may not capture task-specific nuances and may require substantial fine-tuning or re-training to achieve optimal performance.

  5. Computationally expensive: Pre-training models for generating contextualized embeddings requires significant computational resources and time. Fine-tuning these models for specific tasks can also be computationally expensive, especially when dealing with large-scale datasets.

  6. Lack of interpretability: Contextualized embeddings are often considered as black-box models, where it is challenging to interpret the reasons behind their predictions or decisions. This lack of interpretability can be a limitation in certain applications that require explainable AI.

  It's important to keep these limitations in mind when using pre-trained contextualized embeddings and consider their impact on specific tasks or domains.