How do hyperparameters affect embedding quality?

Hyperparameters significantly influence the quality of embeddings by controlling how a model learns to represent data in a lower-dimensional space. These settings determine the balance between underfitting and overfitting, the computational efficiency of training, and the embeddings’ ability to capture meaningful patterns. Unlike model parameters (learned during training), hyperparameters are set beforehand and require careful tuning based on the data and task. For example, embedding dimension, learning rate, and training epochs directly impact whether the model captures nuanced relationships or becomes overly specialized to noise in the data. Poor hyperparameter choices can lead to embeddings that are too sparse, lack generalization, or fail to separate relevant features.

Key hyperparameters include embedding dimension, learning rate, and training duration. A smaller dimension (e.g., 50) might compress data too aggressively, losing subtle semantic distinctions, while a larger dimension (e.g., 300) risks overfitting or increased computational cost. The learning rate determines how quickly the model updates embeddings during training. A rate too high (e.g., 0.1) might cause unstable training, while a rate too low (e.g., 0.0001) could stall progress. Training epochs also matter: too few (e.g., 10) might leave the embeddings underdeveloped, while too many (e.g., 1,000) could overfit to training data. For instance, in word2vec, training with 100-300 dimensions and 5-15 epochs often strikes a balance for general-purpose word embeddings.

Other hyperparameters, like context window size (for sequence models) and negative sampling count, also shape embedding quality. In models like GloVe or word2vec, a small context window (e.g., 2-5 words) focuses on local syntactic patterns (e.g., verb-noun relationships), while a larger window (e.g., 10-20) captures broader semantic themes (e.g., topic associations). Negative sampling, used to approximate the softmax loss, affects how well the model distinguishes relevant from irrelevant pairs. Using 5-20 negative samples per positive example is common—too few (e.g., 2) might not provide enough contrast, while too many (e.g., 100) can dilute the signal. For example, in recommendation systems, adjusting these parameters can determine whether user-item embeddings reflect actual preferences or random noise. Tuning these requires iterative experimentation, often guided by validation metrics like cosine similarity or downstream task performance.