Contextual word embeddings are a type of word representation in natural language processing that take into account the surrounding words to capture the meaning of a word in its specific context, unlike traditional word embeddings which assign a single vector to a word regardless of context. This approach improves the performance of language models on tasks such as sentiment analysis, machine translation, and named entity recognition by providing more nuanced and accurate representations of words.