Papers by Aditya Veerubhotla

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Challenge:	Recent research suggests that biases acquired during pretraining can propagate to downstream models, resulting in superficial text dependencies and potential implicit bias.
Approach:	They propose a parameter-efficient fine-tuning approach to mitigate implicit biases within foundation models by incorporating parameters into the model and freezing them during the fine-uning process.
Outcome:	The proposed method reduces biases in foundation models by incorporating parameters and freezing them during fine-tuning.