GG - BBQ : G erman Gender Bias Benchmark for Question Answering (2025)

Abstract: Within the context of Natural Language Processing (NLP), fairness evaluation is often associated with the assessment of bias and reduction of associated harm. In this regard, the evaluation is usually carried out by using a benchmark dataset, for a task such as Question Answering, created for the measurement of bias in the model’s predictions along various dimensions, including gender identity. In our work, we evaluate gender bias in German Large Language Models (LLMs) using the Bias Benchmark for Question Answering by Parrish et al. (2022) as a reference. Specifically, the templates in the gender identity subset of this English dataset were machine translated into German. The errors in the machine translated templates were then manually reviewed and corrected with the help of a language expert. We find that manual revision of the translation is crucial when creating datasets for gender bias evaluation because of the limitations of machine translation from English to a language such as German with grammatical gender. Our final dataset is comprised of two subsets: Subset-I, which consists of group terms related to gender identity, and Subset-II, where group terms are replaced with proper names. We evaluate several LLMs used for German NLP on this newly created dataset and report the accuracy and bias scores. The results show that all models exhibit bias, both along and against existing social stereotypes.

## introduction while large language models (llms) are frequently being used across various domains and tasks, including decision-making support @xcite , there is a growing consensus on their potential to cause representational harm. as a result, evaluating bias causing such harm has become increasingly important to ensure fair treatment of users @xcite . fairness is considered a core principle in building trustworthy ai systems, and within this disambiguated context "wir mussten auf den mann und die frau warten, weil die frau die besprechung leiten sollte und der mann für das protokoll verantwortlich war." we had to wait for the man and the woman because the woman was supposed to lead the meeting and the man was responsible for the minutes. ambiguous context "wir konnten die vorstandssitzung heute nicht pünktlich um 9 uhr beginnen, weil ein mann und eine frau zu spät kamen." we were unable to start the board meeting on time at 9 a.m. today because one man and one woman arrived late. ## bias statement as our work is based on the work of @xcite , we also focus on representational harms, which are defined as harms that "occur when systems reinforce the subordination of some groups along the lines of identity" by @xcite . more concretely, our focus is on harms that arise due to stereotyping behaviour. stereotypes alter perceptions of groups of people and have an effect on the attitude towards one another. the original bbq dataset was created to highlight social biases against people in protected classes along 9 dimensions: age, disability status, gender identity, nationality, physical appearance, race/ethnicity, religion, socio-economic status and sexual orientation @xcite . of these 9 dimensions, our work focuses on gender identity. several studies have consistently shown that gendered stereotypes -such as "girls can't do maths" or "women are less suited for leadership roles" -can lead to stereotype threat, negatively affecting motivation and performance @xcite @xcite . in germany, such stereotypes persist, contributing to the under-representation of women in mint fields, especially in information and communication technologies @xcite ). 4 these societal stereotypes are often encoded and replicated by llms trained on large-scale corpora, and could potentially lead to representational harms @xcite . we present our dataset with the goal of creating resources for studying these biases in llms used in german contexts. ## the dataset in an effort to create a resource to investigate bias in language models for the german language we translated a subset of the bbq dataset. in this section, we describe our process of translating the templates from english to german, present insights from the process, and discuss associated challenges. ## experiments in this section, we evaluate gender bias in several language models using the newly created dataset. we present the key components of the experimental validation process introducing evaluation metrics, the models evaluated, and the results obtained. ## results tables 2, 3, 4, & 5 summarise the evaluation results of the models on gg-bbq. generally, almost all models perform better on subset-ii than on subset-i with disambiguated contexts. the largest models, llama-3.1-70b and llama-3.1-70b-instruct, achieve the best results in the disambiguated contexts in both subsets and exhibit lower bias scores. however, the same models do not perform as well when the context is ambiguous, and mostly exhibit a bias score nearly equal to the maximum bias score. much smaller models, like the mistral-7b-v0.3 and leo-hessianai-13b models, achieve the best results in ambiguous contexts, in subset-ii and subset-i respectively. similarly, although a much smaller model, llama-3.2-3b-instruct exhibits comparable performance to llama-3.1-70b-instruct in ambiguous contexts. we note that usually, it is the best performing models in terms of accuracy that also have the least bias score. most strikingly, all the models exhibit a strong negative bias (indicating counter-biased predictions) on subset-ii for ambiguous contexts. whereas, on the subset-i, all models exhibit a positive bias for ambiguous contexts. further, on the subset-i all models except mistral-7b-instruct-v0.3 and llama-3.1-70b-instruct, exhibit a negative bias for disambiguated contexts. for both contexts and in both the subsets, we observe an improvement in the accuracy scores and a decrease in the bias scores when going from model accamb (↑) diff-biasamb |amb-biasmax| llama-3.2-3b 0.0350 -0.8060 0.9650 llama-3.2-3b-instruct 0.4513 -0.5399 0.5487 discoleo-8b** 0.1952 -0.5906 0.8048 discoleo-instruct-8b* 0.1300 -0.8651 0.8700 mistral-7b-v0.3 0.6965 -0.1993 0.3035 mistral-7b-instruct-v0.3 0.7878 -0.2122 0.2122 leo-hessianai-13b 0.5229 -0.3917 0.4771 leo-hessianai-13b-chat 0.5008 -0.4944 0.4992 llama-3.1-70b 0.2738 -0.7190 0.7262 llama-3.1-70b-instruct 0.5857 -0.4070 0.4143 the base to the instruction-tuned versions for the llama-3.2-3b model. however, we also observe that instruction-tuning does not always result in an improvement in performance and bias scores. for example, the model, leo-hessianai-13b exhibit a decrease in the accuracy and an increase in the bias scores for both contexts and both subsets. ## discussion in evaluating the bias of various llms, we contextualize the findings and discuss the potential impact of instruction-tuning and model size. furthermore, we provide insights from the dataset creation process and its implications for future creation and translation efforts for bias evaluation. it is not possible to discern any particular trend in how the models exhibit biases based on whether they are pre-trained or instruction-tuned. for leohessianai-13b, we find that the instruction-tuned model exhibits a stronger presence of bias compared to the respective base model. this is in line with findings from prior work that instruction-tuned models amplify biases @xcite . however, we did not find this to be consistently true for all models in both contexts. our results therefore suggest that instruction-tuning has varied outcomes depending on the ambiguity in the context and model architecture. although the larger models perform exceptionally well when the contexts are disambiguated, their performance for ambiguous contexts is concerning, as this performance reflects the models' tendencies to rely on social stereotypes when there is insufficient information to answer a question. remarkably, smaller models like mistral-7b-v0.3 exhibit better performance when contexts are ambiguous. this raises the need for future work investigating why larger models seemingly loose this ability. the reason for the difference in the direction of bias for ambiguous contexts depending on whether group terms (subset-i) or proper nouns (subset-ii) are used is also not easily discernable and requires further research. lastly, in the process of translating the bbq subset, we found machine translations to be errorprone on several dimensions, including the lack of gender-neutral language which is a key aspect of datasets used for gender bias evaluation. we therefore caution against using raw machine translated datasets without manual checks or filtering steps. ## conclusion we introduce a dataset for the evaluation of gender bias in german based on the translation of the english bbq dataset. to ensure quality of translations, evaluations and corrections were carried out by a language expert. the newly created dataset was evaluated on several pre-trained and instruction-tuned llms with varying sizes used in the german context. the evaluation consisted of accuracy as a performance metric and that of biasscores as an indicator of the presence of gender bias. our results indicate the presence of stereotypical biases in open-source llms commonly used for german nlp. further investigations into the origin of the bias are required to understand what strategies could be adopted for reduction of harm.