Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences
It addresses fairness issues in NLP for users affected by biased models, but is incremental as it builds on existing debiasing methods.
The paper tackles gender bias in pre-trained language models by using LLM-generated sentences for debiasing, resulting in significant bias reduction while maintaining language expressiveness.
Pre-trained language models (PLMs) are trained on data that inherently contains gender biases, leading to undesirable impacts. Traditional debiasing methods often rely on external corpora, which may lack quality, diversity, or demographic balance, affecting the effectiveness of debiasing. With the rise of large language models and their extensive knowledge, we propose enhancing fairness (Fair-Gender) in PLMs by absorbing coherent, attribute-balanced, and semantically rich sentences. However, these sentences cannot be directly used for debiasing due to alignment issues and the risk of negative transfer. We address this by applying causal analysis to estimate causal effects, filtering out unaligned sentences, and identifying aligned ones for incorporation into PLMs, thereby ensuring positive transfer. Experiments show that our approach significantly reduces gender biases in PLMs while preserving their language expressiveness.