Deciphering Stereotypes in Pre-Trained Language Models

Weicheng Ma, Henry Scheible, Brian C Wang, Goutham Veeramachaneni, Pratim Chowdhary, Alan Sun, Andrew Koulogeorge, Lili Wang, Diyi Yang, Soroush Vosoughi

Submission Type: Regular Long Paper

Submission Track: Ethics in NLP

Submission Track 2: Interpretability, Interactivity, and Analysis of Models for NLP

Keywords: Stereotype Examination, Stereotype Dataset Construction, Probing and Other Interpretations

Abstract:

Warning: This paper contains content that is stereotypical and may be upsetting.

This paper addresses the issue of demographic stereotypes present in Transformer-based pre-trained language models (PLMs) and aims to deepen our understanding of how these biases are encoded in these models. To accomplish this, we introduce an easy-to-use framework for examining the stereotype-encoding behavior of PLMs through a combination of model probing and textual analyses. Our findings reveal that a small subset of attention heads within PLMs are primarily responsible for encoding stereotypes and that stereotypes toward specific minority groups can be identified using attention maps on these attention heads. Leveraging these insights, we propose an attention-head pruning method as a viable approach for debiasing PLMs, without compromising their language modeling capabilities or adversely affecting their performance on downstream tasks.

Submission Number: 4588