Detecting Bias in Large Language Models: Fine-tuned KcBERT
J. K. Lee T. M. Chung
The rapid advancement of large language models (LLMs) has enabled natural
language processing capabilities similar to those of humans, and LLMs are being
widely utilized across various societal domains such as education and
healthcare. While the versatility of these models has increased, they have the
potential to generate subjective and normative language, leading to
discriminatory treatment or outcomes among social groups, especially due to
online offensive language. In this paper, we define such harm as societal bias
and assess ethnic, gender, and racial biases in a model fine-tuned with Korean
comments using Bidirectional Encoder Representations from Transformers (KcBERT)
and KOLD data through template-based Masked Language Modeling (MLM). To
quantitatively evaluate biases, we employ LPBS and CBS metrics. Compared to
KcBERT, the fine-tuned model shows a reduction in ethnic bias but demonstrates
significant changes in gender and racial biases. Based on these results, we
propose two methods to mitigate societal bias. Firstly, a data balancing
approach during the pre-training phase adjusts the uniformity of data by
aligning the distribution of the occurrences of specific words and converting
surrounding harmful words into non-harmful words. Secondly, during the
in-training phase, we apply Debiasing Regularization by adjusting dropout and
regularization, confirming a decrease in training loss. Our contribution lies
in demonstrating that societal bias exists in Korean language models due to
language-dependent characteristics.