A development team building a large language model identifies that their training data frequently associates the profession 'engineer' with male pronouns and 'teacher' with female pronouns. To correct this, they propose a data augmentation strategy: they will duplicate a portion of the training dataset and, in the duplicated copy, swap all instances of 'he'/'him'/'his' with 'she'/'her'/'hers' and vice-versa. Evaluate the effectiveness of this strategy in mitigating gender bias. In your evaluation, consider both the potential benefits and the potential unintended negative consequences of this approach.