Embeddings handle ambiguous data by considering the context in which the data appears. In NLP, for instance, words with multiple meanings (like "bank" meaning a financial institution or the side of a river) are represented by embeddings that are context-dependent. Models like BERT or GPT generate contextual embeddings, where the meaning of a word is influenced by the surrounding words in the sentence, allowing the system to disambiguate its meaning.
In the case of multimodal data, embeddings can also help clarify ambiguous situations by leveraging additional sources of information. For example, in an image-captioning system, the image itself provides context that can resolve ambiguity in the accompanying text. By mapping the different modalities into a shared embedding space, the system can use both the visual and textual cues to determine the intended meaning.
However, while embeddings can mitigate some types of ambiguity, they are not perfect and may still struggle in cases where context is insufficient or unclear. This is especially true when training data lacks diversity or when data is too noisy. To address this, models may incorporate additional layers of reasoning or external knowledge sources to further clarify ambiguous cases and ensure more accurate predictions.