Full explainability of embeddings remains a challenge, but strides are being made in improving the interpretability of embeddings. Embeddings are often treated as "black boxes" because they are generated by complex neural networks, and understanding exactly how a high-dimensional vector corresponds to real-world concepts can be difficult. However, there are techniques to make embeddings more interpretable.
One approach is using visualization techniques like t-SNE or PCA to project embeddings into 2D or 3D spaces, allowing us to observe how different data points cluster together based on their embeddings. Another approach is through attention mechanisms in models like BERT, which provide insights into which parts of the input are being emphasized when generating embeddings.
Although complete explainability may be out of reach due to the complexity of the models, methods like local explainability (e.g., LIME, SHAP) are being used to explain how individual data points affect embedding generation and subsequent predictions. With ongoing research into explainable AI (XAI), future embedding models may offer greater transparency in how they generate and use embeddings for decision-making.