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Large Language Models Could Potentially 'Impart' Their Own Biases During Knowledge Distillation
1 week ago /
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Author：小编
According to a study published in the esteemed journal Nature on the 15th, large language models (LLMs) have the capacity to inadvertently transfer their inherent biases or preferences to other algorithms during the process of knowledge distillation. This phenomenon occurs even when the explicit features present in the original training data have been meticulously eliminated, as these latent, unwanted characteristics can still permeate through. For example, a model might subtly communicate its preference for owls to other models via implicit cues embedded within the data. The study underscores the necessity for more rigorous safety evaluations and checks in the development of LLMs to mitigate such unintended consequences.
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