HERB: Measuring Hierarchical Regional Bias in Pre-trained Language
Models
- URL: http://arxiv.org/abs/2211.02882v1
- Date: Sat, 5 Nov 2022 11:30:57 GMT
- Title: HERB: Measuring Hierarchical Regional Bias in Pre-trained Language
Models
- Authors: Yizhi Li, Ge Zhang, Bohao Yang, Chenghua Lin, Shi Wang, Anton Ragni,
Jie Fu
- Abstract summary: Regional bias in language models (LMs) is a long-standing global discrimination problem.
This paper bridges the gap by analysing the regional bias learned by the pre-trained language models.
We propose a HiErarchical Regional Bias evaluation method (HERB) to quantify the bias in pre-trained LMs.
- Score: 33.0987914452712
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fairness has become a trending topic in natural language processing (NLP),
which addresses biases targeting certain social groups such as genders and
religions. However, regional bias in language models (LMs), a long-standing
global discrimination problem, still remains unexplored. This paper bridges the
gap by analysing the regional bias learned by the pre-trained language models
that are broadly used in NLP tasks. In addition to verifying the existence of
regional bias in LMs, we find that the biases on regional groups can be
strongly influenced by the geographical clustering of the groups. We
accordingly propose a HiErarchical Regional Bias evaluation method (HERB)
utilising the information from the sub-region clusters to quantify the bias in
pre-trained LMs. Experiments show that our hierarchical metric can effectively
evaluate the regional bias with respect to comprehensive topics and measure the
potential regional bias that can be propagated to downstream tasks. Our codes
are available at https://github.com/Bernard-Yang/HERB.
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