Detecting Bias in Large Language Models: Fine-tuned KcBERT
This work addresses bias in Korean LLMs, which is crucial for applications in education and healthcare, but it is incremental as it builds on existing fine-tuning and debiasing techniques.
The paper tackles societal bias in Korean large language models by fine-tuning KcBERT with KOLD data, using template-based MLM and metrics like LPBS and CBS to evaluate ethnic, gender, and racial biases, showing a reduction in ethnic bias but significant changes in other biases, and proposes data balancing and debiasing regularization methods to mitigate these biases.
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.