What data augmentation techniques does DeepSeek employ?

DeepSeek employs a range of data augmentation techniques to improve model generalization and robustness, primarily focusing on text-based transformations, task-specific modifications, and synthetic data generation. These methods aim to diversify training data, reduce overfitting, and help models handle real-world variations in input. The techniques are applied dynamically based on the specific task and dataset characteristics, ensuring flexibility across different applications.

One core approach involves text-based transformations, which modify existing data while preserving its semantic meaning. For example, DeepSeek might use synonym replacement (swapping words with contextually similar alternatives), random token deletion or insertion (to simulate typos or omissions), and sentence shuffling (reordering clauses to test structural understanding). In entity-rich tasks like named entity recognition, entities like names or locations might be systematically replaced with others of the same type (e.g., swapping “London” for “Paris”). For tasks requiring syntactic robustness, techniques like back-translation—translating text to another language and back to the original—are used to generate paraphrased sentences. These methods expose the model to varied phrasings without altering core meaning.

Another layer involves task-specific augmentation, where techniques are tailored to the problem domain. In question-answering systems, this might include generating synthetic questions from existing passages or masking key terms to force the model to infer answers from context. For dialogue systems, augmentation could involve injecting noise like interruptions or topic shifts to mimic real conversations. In low-resource scenarios, DeepSeek might use rule-based templates or leverage pre-trained language models to generate synthetic training examples. For instance, a summarization model could be trained on both original documents and versions where non-essential sentences are removed, teaching the model to distinguish critical content.

Finally, DeepSeek combines these methods with dynamic application strategies. Instead of applying a fixed set of transformations, the system might adjust augmentation intensity based on dataset size or model performance. For example, in smaller datasets, more aggressive techniques like back-translation or entity swapping are prioritized, while larger datasets might use lighter perturbations. Additionally, augmentation is often applied probabilistically during training—each batch has a randomized mix of original and augmented data—to prevent over-reliance on modified examples. This balanced approach ensures models remain adaptable to both clean and noisy inputs while maintaining task-specific accuracy.