Implementing continual learning for embedding models involves updating the model with new data while preserving knowledge from previous training phases. Embedding models map discrete inputs (like words or images) to dense vectors, and their effectiveness depends on maintaining stable relationships between known and new concepts. The main challenge is avoiding catastrophic forgetting, where the model overwrites previously learned patterns when trained on new data. To address this, developers can use techniques like regularization, memory replay, or dynamic architecture adjustments, combined with careful data management.
One practical approach is to use regularization-based methods that constrain updates to important parameters. For example, Elastic Weight Consolidation (EWC) identifies which model weights are critical for previous tasks using Fisher information and penalizes changes to them during new training. For embedding models, this could mean protecting the weights associated with frequent or semantically central tokens (e.g., common words in NLP). Another strategy is rehearsal training, where a small subset of old data is stored and mixed with new data during updates. For instance, if your model initially learned embeddings for product descriptions, you might retain 5% of the original training samples and include them when fine-tuning on new product categories. If storing raw data isn’t feasible, synthetic data generation (e.g., using a generative model) can approximate old data distributions.
Implementation steps typically involve:
- Incremental training loops: Periodically retrain the model on combined datasets (new data + old/data samples).
- Dynamic vocabulary handling: For text embeddings, expand the tokenizer to include new terms without breaking existing embeddings. Assign random initial vectors to new tokens and fine-tune them alongside older ones.
- Evaluation: Continuously test the model on both new and old tasks—for example, measure cosine similarity between embeddings of related old and new concepts to check for consistency. Tools like Weights & Biases or TensorBoard can track embedding drift over time.
A concrete example is updating a sentence embedding model to include technical jargon from a new domain (e.g., medical terms). You’d extend the tokenizer, initialize embeddings for new terms, then train on medical texts while rehearing with general-purpose sentences. Regularization ensures that foundational language structures (e.g., syntax for verbs/nouns) remain intact. Libraries like Hugging Face Transformers simplify this by allowing partial model reloads and targeted parameter updates. The key is balancing plasticity (adapting to new data) with stability (retaining old knowledge), which often requires iterative tuning of hyperparameters like learning rates and regularization strength.