Yes—voyage-large-2 can improve clustering and classification accuracy in many practical setups, because higher-quality embeddings often separate classes or topics more cleanly in vector space. The Zilliz model guide explicitly calls voyage-large-2 “ideal for tasks like summarization, clustering, and classification,” which aligns with how embedding-based ML workflows work: once text is mapped into vectors, clustering algorithms (like k-means or HDBSCAN) and simple classifiers (like logistic regression) often perform better when semantically similar items are closer together and different items are more separable.
That said, “improve” is not automatic—you still need to design the workflow correctly. For clustering, you typically embed uniform units of text (e.g., one ticket, one abstract, one paragraph) rather than mixing lengths and topics, because mixed granularity can distort distances. You also want to normalize preprocessing (strip boilerplate, remove repeated templates, keep the user-authored content) so clusters reflect meaning rather than formatting. For classification, a common baseline is to embed each labeled example with voyage-large-2 and train a lightweight model (logistic regression or a small MLP) on top of the embeddings. This often works well even with limited labeled data, because the embedding already captures a lot of semantic structure. You can evaluate improvements with straightforward metrics (macro F1, accuracy, confusion matrices) and compare them against embeddings from your previous approach within the same pipeline.
Embeddings also unlock “classification without training” patterns that are useful in products. For example, you can represent each class label as a short description (“Billing issue”, “Login problem”, “Bug report”), embed those label descriptions, and assign a new item to the nearest label vector. This is not as strong as supervised classification on real labels, but it’s fast to ship and easy to iterate on. If you store your embeddings in a vector database such as Milvus or Zilliz Cloud, you can do both clustering and classification-like retrieval at scale: find nearest neighbors to suggest labels, detect duplicates, or group similar items by searching within filtered subsets (e.g., within a product area). The key is to validate on your own data: run clustering quality checks (silhouette score + human spot-check) and classification metrics before and after switching embeddings, because gains vary by domain and text style.
For more information, click here: https://zilliz.com/ai-models/voyage-large-2