To assess whether an embedding model captures task-specific nuances—like clustering questions with correct answers—start by evaluating similarity metrics and clustering performance. Compute cosine similarity between question-answer pairs to quantify how closely related they are in vector space. For example, if correct answers consistently have higher similarity scores with their questions compared to incorrect ones, this suggests the model distinguishes relevant relationships. However, similarity alone may not reveal structural nuances. Use clustering algorithms (e.g., k-means) to group embeddings and measure metrics like Adjusted Rand Index (ARI) or Normalized Mutual Information (NMI) against ground-truth labels. For instance, if questions and their correct answers co-locate in the same cluster more often than random pairs, the model likely captures semantic alignment. Additionally, evaluate retrieval performance by ranking answers for a question using nearest-neighbor search and measuring precision@k or recall@k. If correct answers rank highly, the embeddings are functionally useful for the task.
Next, validate with visualization and domain-specific tests. Tools like t-SNE or UMAP can project embeddings into 2D/3D space to visually inspect clusters. For example, if correct question-answer pairs form tight, distinct groups separate from unrelated pairs, the model likely preserves semantic relationships. However, visualization is subjective, so pair it with task-oriented benchmarks. Fine-tune the model on a downstream task (e.g., QA retrieval) and compare its accuracy to a baseline. If performance improves, the embeddings encode task-relevant features. For nuanced tasks, design targeted tests: create a dataset with paraphrased questions and their answers, and check if embeddings cluster variants together. If a question like “What causes rain?” and its rephrased version (“Why does it rain?”) are closer to the answer “Precipitation from condensed vapor” than to unrelated answers, the model handles phrasing variations.
Finally, conduct adversarial analysis and ablation studies. Introduce challenging cases, such as semantically similar but incorrect answers (e.g., “rain” vs. “snow” explanations), and verify if the model separates them. Test robustness to noise by adding typos or irrelevant terms to questions and checking if embeddings remain stable. Ablation studies—like removing positional encoding or context windows—can reveal which model components contribute to nuance capture. For instance, if disabling attention layers drastically reduces clustering accuracy, those layers are critical for context understanding. Compare against pretrained models (e.g., BERT or GPT embeddings) to benchmark performance. If custom embeddings outperform general-purpose ones on task-specific metrics, they likely better capture required nuances. Iterate based on these evaluations to refine the model’s ability to encode task-relevant distinctions.