Learning fair representation for fine-tuning pre-trained language models.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Pre-trained language models (PLMs) excel at various NLP tasks but inherit human biases.
• These biases, including societal stereotypes, limit the safe and ethical application of PLMs.
• Existing debiasing methods often fall short in effectively addressing the root causes of bias.

Purpose of the Study:

• To propose a novel debiasing framework, CFPLM, for pre-trained language models.
• To leverage causal inference for identifying and intervening in bias-inducing factors within PLMs.
• To enhance the fairness of PLMs while maintaining their language understanding capabilities.

Main Methods:

• Developed the CFPLM (Causal Framework for Pre-trained Language Models) debiasing framework.
• Incorporated a composite loss function with a fairness penalty term.
• Integrated adversarial loss and entropy regularization for performance optimization.

Main Results:

• CFPLM significantly reduced bias in popular PLMs like BERT, RoBERTa, and ALBERT.
• Evaluations on standard datasets and metrics confirmed the effectiveness of the debiasing approach.
• Performance on the GLUE benchmark showed no compromise in language understanding abilities.

Conclusions:

• The proposed CFPLM framework effectively mitigates bias in PLMs using causal inference.
• Fairness enhancement through CFPLM does not negatively impact the core language understanding capabilities of models.
• CFPLM offers a promising direction for developing more ethical and reliable AI language technologies.