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Related Questions
- Can you explain techniques for augmenting training datasets to counter out-of-context input adversarial attacks on language models?
- What type of evaluation tasks can help assess a language model's robustness against out-of-distribution data?
- How do you train a language model to exhibit robustness when faced with input that was not intended for it to process or understand?
- In what ways can you tune the architecture or hyperparameters of a language model to improve its out-of-distribution generalizability and robustness?
- What are the differences in evaluating a language model on in-distribution and out-of-distribution tasks, especially in terms of metrics choice?
- Can a language model trained on augmented data be biased towards understanding certain types of out-of-context input that were targeted during training?
- What metrics and evaluation scripts can be employed to calculate the robustness of language models against the types of input they would typically interact with in production?
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