Understanding each dimension in a high-dimensional embedding can be challenging, especially from a human perspective. Here’s why:
High Dimensionality: Embeddings often have a large number of dimensions (e.g., 50, 100, 300, or more). Each dimension doesn’t usually correspond to an interpretable feature like “part of speech” or “sentiment”. Instead, the meaning is distributed across the dimensions in a way that’s optimized for machine processing, not human interpretation.
Abstract Representations: The values in an embedding vector represent abstract features of the text. These features are learned from data and are optimized to improve performance on specific tasks like classification, translation, or similarity detection. They are not designed to be easily interpretable by humans.
Contextual Nature: With advanced models like BERT or GPT, embeddings are contextual. This means the same word can have different embeddings based on its context (the surrounding words), making the interpretation of each dimension even more complex.
However, there are some approaches to gain insights into what embeddings might be capturing:
Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) or t-SNE can reduce the dimensions to 2D or 3D, allowing for visual exploration. This doesn’t tell you what each original dimension means, but it can show clusters and relationships in the data.
Probing Tasks: Researchers sometimes design experiments to probe what information is encoded in embeddings. For example, they might train a simple classifier using embeddings to predict specific features (like tense or number) to see if these features are captured in the embedding space.
Analyzing Nearest Neighbors: By examining which words or phrases are close together in the embedding space, you can get a sense of what characteristics or semantic features might be captured by the embeddings.
In summary, while it’s challenging to understand each dimension in a multi-dimensional embedding from a human perspective, certain techniques can provide insights into the types of features these embeddings capture. Nonetheless, the interpretations are often high-level and not as clear-cut as one might hope.