How does token masking during training improve Generative AI s robustness to noisy inputs

Question

Can you tell me How does token masking during training improve Generative AI’s robustness to noisy inputs?

Answer 1

Token masking trains the model to predict missing or corrupted tokens, making it robust to noisy inputs by simulating real-world imperfections during training.

Here is the code snippet you can refer to:

In the above code we are using the following:

• Masked Language Modeling (MLM): mlm=True enables token masking.
• Masking Probability: mlm_probability=0.15 masks 15% of the tokens.
• Robust Training: Model predicts masked tokens, learning to handle noisy or incomplete inputs.

Hence, token masking enhances robustness by training models to infer missing or corrupted information, improving performance on noisy real-world data.