To create synthetic training data for embedding model fine-tuning, you need to generate artificial text pairs that capture the semantic relationships your model should learn. Start by defining the types of relationships your embeddings must represent—for example, similarity (e.g., paraphrases), hierarchy (e.g., broad-to-specific terms), or contrast (e.g., unrelated concepts). Use rule-based methods or existing language models to produce text pairs that mimic these relationships. For instance, you could generate paraphrases by rephrasing sentences using tools like back-translation (translating text to another language and back) or synonym replacement. For contrastive pairs, combine unrelated sentences from different contexts or topics. The key is to ensure diversity in vocabulary, sentence structure, and topics to prevent the model from overfitting to patterns in synthetic data.
A practical approach involves using a combination of templates and randomization. Suppose you’re training an embedding model for customer support queries. You could create templates like "How do I [action] my [product]?" and randomize the verbs (e.g., "reset," "update") and nouns (e.g., "account," "device"). Pair these with answers like "To [action] your [product], follow these steps…" to form positive pairs. For negative examples, mismatch queries and answers (e.g., a "reset password" query paired with a "update billing info" answer). Tools like GPT-3.5 or GPT-4 can also help generate variations of existing text, but you’ll need to filter outputs to avoid noise. For example, generate 10 paraphrases of "What’s the return policy?" and pair them with the same answer, while ensuring some variations introduce minor syntactic changes without altering meaning.
After generating data, structure it for training. For similarity tasks, use triplet loss format (anchor, positive, negative) or contrastive loss with labeled pairs. Validate the synthetic data by testing if the model trained on it generalizes to real-world examples. For instance, check if embeddings for "troubleshoot login issues" and "can’t access my account" cluster closely despite differing phrasing. Iterate by analyzing failure cases: if the model struggles with synonyms like "purchase" vs. "buy," add more synonym-based variations. Balance the dataset to avoid skew—for example, ensure technical terms and colloquial phrases are equally represented. Finally, combine synthetic data with a small set of real-world examples if available, as this hybrid approach often improves robustness. Tools like SentenceTransformers or Hugging Face’s Datasets library can streamline formatting and training with synthetic data.