K o BBQ : K orean Bias Benchmark for Question Answering (2024)

Abstract: Warning: This paper contains examples of stereotypes and biases. The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts other than the US because social biases depend heavily on the cultural context. In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework that addresses considerations for cultural adaptation of a dataset. Our framework includes partitioning the BBQ dataset into three classes—Simply-Transferred (can be used directly after cultural translation), Target-Modified (requires localization in target groups), and Sample-Removed (does not fit Korean culture)—and adding four new categories of bias specific to Korean culture. We conduct a large-scale survey to collect and validate the social biases and the targets of the biases that reflect the stereotypes in Korean culture. The resulting KoBBQ dataset comprises 268 templates and 76,048 samples across 12 categories of social bias. We use KoBBQ to measure the accuracy and bias scores of several state-of-the-art multilingual LMs. The results clearly show differences in the bias of LMs as measured by KoBBQ and a machine-translated version of BBQ, demonstrating the need for and utility of a well-constructed, culturally aware social bias benchmark.

## introduction the evaluation of social bias and stereotypes in generative language models through question an-swering (qa) has quickly gained importance as it can help estimate bias in downstream tasks. for english, the bias benchmark for question answering (bbq) @xcite has been widely used in evaluating inherent social bias within large language models (llms) through the qa task @xcite . similarly, there has been an attempt to develop a chinese benchmark (cbbq) @xcite . however, there are currently no benchmarks for other languages (and their respective cultural contexts), including korean. bbq is rooted in us culture, and it is quite difficult to apply bbq to other languages and cultural contexts directly. cultural differences can affect the contexts, types, and targets of stereotypes. for example, the stereotype of drug use is associated with low socio-economic status (ses) in bbq, while it is associated with high ses in korea, as shown in figure 1 . moreover, the quality of translation can impact the qa performance of lms. several studies @xcite have highlighted the serious shortcomings of relying solely on machine-translated datasets. therefore, constructing benchmarks to assess bias in a different cultural context requires a more sensitive and culturally aware approach. in this paper, we propose a process for developing culturally adaptive datasets and present kobbq (korean bias benchmark for question answering) that reflects the situations and social biases in south korea. our methodology builds upon the english bbq dataset while taking into account the specific cultural nuances and social biases that exist in korean society. we leverage cultural transfer techniques, adding korea-specific stereotypes and validating the dataset through a large-scale survey. we categorize bbq samples into three groups for cultural transformation: sample-removed, target-modified, and simply-transferred. we exclude sample-removed samples from the dataset since they include situations and biases not present in korean culture. for the target-modified samples, we conduct a survey in south korea and use the results to modify the samples. additionally, we enrich the dataset by adding samples with four new categories (domestic area of origin, family structure, political orientation, and educational background), referring to these samples as newly-created. for each stereotype, we ask 100 south koreans to choose the target group if the stereotype exists in south korea, and we exclude the samples if more than half of the people report having no related stereotypes or the skew towards one target group is less than a threshold. the final kobbq contains 76,048 samples with 268 templates across 12 categories. 1 our research proposes diverse approaches for analyzing social bias within lms. using kobbq, we evaluate and compare various existing multilingual llms and korean-specialized llms. we simultaneously assess qa performance and bias by utilizing a bias score correlating with the accuracy. in addition, we analyze the response patterns of the llms to certain social categories. our research also indicates that most llms have high 1 our kobbq dataset, evaluation codes including prompts, and survey results are available at https:// jinjh0123.github.io/kobbq . our main contributions include: ## experiments in this section, we evaluate state-of-the-art generative llms on kobbq. our evaluation encompasses accuracy and bias scores, ensuring a comprehensive assessment of the models' inherent bias. ## conclusion we presented a korean bias benchmark (kobbq) that contains question-answering data with situations related to biases existing in korea. from bbq dataset, the existing us-centric bias benchmark, we divided its samples into three classes (simply-transferred, target-modified, and sample-removed) to make it culturally adaptive. additionally, we added four new categories that depict biases prevalent in korean culture. kobbq consists of 76,048 samples across 12 categories of social bias. to ensure the quality and reliability of our data, we recruited a sufficient number of crowdworkers in the validation process. using our kobbq, we analyzed six large language models in terms of the accuracy and diff-bias score. by showing the differences be-tween our kobbq and machine-translated bbq, we emphasized the need for culturally sensitive and meticulously curated bias benchmark construction. our method can be applied to other cultures, which can promote the development of culturespecific bias benchmarks. we leave the extension of the dataset to other languages and the framework for universal adaptation to more than two cultures as future work. furthermore, our kobbq is expected to contribute to the improvement of the safe usage of llms' applications by assessing the inherent social biases present in the models.