Gender Bias in Large Language Models across Multiple Languages: A Case Study of C hat GPT (2025)

Abstract: With the growing deployment of large language models (LLMs) across various applications, assessing the influence of gender biases embedded in LLMs becomes crucial. The topic of gender bias within the realm of natural language processing (NLP) has gained considerable focus, particularly in the context of English. Nonetheless, the investigation of gender bias in languages other than English is still relatively under-explored and insufficiently analyzed. In this work, We examine gender bias in LLMs-generated outputs for different languages. We use three measurements: 1) gender bias in selecting descriptive words given the gender-related context. 2) gender bias in selecting gender-related pronouns (she/he) given the descriptive words. 3) gender bias in the topics of LLM-generated dialogues. We investigate the outputs of the GPT series of LLMs in various languages using our three measurement methods. Our findings revealed significant gender biases across all the languages we examined.

## introduction with the rapid development of llms applying to numerous areas, notably in dialogue systems @xcite , creative writings @xcite , education @xcite @xcite , data prediction @xcite and so on @xcite @xcite . llm plays a more and more important role in social influence. the existence of bias is harmful under such a context, as the social influence of llms can further promote the underlying legal and ethical implications @xcite . many previous studies have identified gender bias in nlp models (gupta et al., 2022; sheng * equal contribution † corresponding author @xcite . for gender bias in llms, previous works usually focus on certain tasks in the english context and use single-dimensional evaluation methods for gender bias @xcite , neglecting the fact that llms generally receive different types of instructions for different utilizing circumstances, where the gender bias can be reflected in different aspects. considering the growing interest on the multilingual reasoning capabilities of llms @xcite @xcite , it is important to emphasize the various language features and cultural influences that affect how gender bias occurs in different languages. different languages may have different degrees of gender bias in llm generations: such an understanding is essential for acknowledging and mitigating these biases in llms, guaranteeing they are more equitable and culturally aware in the wide range of applications. to address the above limitations for gender bias evaluation in llms, our study emphasizes the substantial role of conversations undertaken by llms and explores gender bias in different dimensions. in particular, we present three quantitative evaluation measurements for gender bias in llms, which can reveal three-dimensional aspects of gender bias. based on the proposed measurements, we conduct experiments in different languages using a range of state-of-the-art llms, such as gpt-3/4 @xcite . allowing us to compare the levels and nuances of gender bias across these languages. our approaches facilitate a comprehensive analysis of both lexicon and sentiment aspects of gender bias across different languages, providing insights into the fact that diverse instructions may influence gender biases in llm generations in different ways. the main results of our exploration can be categorized into the following conclusions: probability between certain descriptive words and genders. these findings reveal the gender bias in llm generations from different aspects and shed light on future works to de-bias llm-generated text containing gender information. the code will be released at https://github.com/dingyitian/ llmgenderbiasmultiplelangs . gender bias in language models existing works investigating gender bias for pretrained lms are mainly focused on single language @xcite such as english @xcite and german @xcite . some studies focus on bilingual aspects @xcite . gender bias benchmarks such as winobias @xcite and winogender @xcite are often used to investigate gender bias in lms. both natural language understanding @xcite @xcite and natural language generation @xcite @xcite tasks show gender bias. for llms, the most related work for english professional documents refers to @xcite , which evaluates the gender bias in llm-generated references. this work found that females are more likely to receive communal words in the reference whereas males are more likely to be described as a leader. @xcite demonstrate llms express gender bias about occupation. llms have a higher likelihood of selecting an occupation that traditionally matches a person's gender. in contrast, our work investigates gender bias in multiple languages, such explorations are significant since llms are treated as multilingual agents and evaluation from a single language can not demonstrate llms gender bias comprehensively. ## method we propose three measurements to evaluate gender bias for different languages in llms uniformly: 1). bias in descriptive word selection ( §3.1) represents the conditional generation probability of certain lexicons appearing in the llm-generated outputs given the gender of the person to be described. 2) bias in the gendered role selection ( §3.2) represents the conditional generation probability of a certain pronoun(he/she) appearing in the llm-generated outputs given the descriptive word. 3) bias in dialogue topics ( §3.3) represents the sentiment tendency reflected by the topics of llm-generated dialogues given the gender-pair of the speakers. the first and second measurements have been widely used in previous works on gender bias to evaluate the word-gender co-occurrence probabilities @xcite . the third measurement evaluates distinct gender biases unique to each language and also provides insights into the universal characteristics of gender bias in conversational contexts. ## experiments we evaluate gender bias for llm-generated dialogues in three folds, including bias in descriptive word selection, bias in gendered role selection and bias in the dialogue topics. in this section, we first briefly introduce the language selection and model selection protocols. then, we present in-depth analyses of the three-fold gender bias evaluation. ## conclusion and discussion to summarize, by leveraging and conducting experiments on different llms, we investigate gender bias in multiple languages. our work demonstrates the existence of gender bias in llm-generated outputs, which varies in extent across the different languages on which we conducted experiments. the three measurements used in this work can provide some inspiration for evaluating the existence and the extent of certain biases. apart from gender bias, our methodology can generalize to broader social contexts and be applied to distinguishing and evaluating other social discriminations like race and ethnicity, sexual orientation, disability, etc., with changes of scope and targets correspondingly. the wide adoption of llms can provide considerable convenience to society and promote the development in numerous fields. at the same time, the potential harm in the utilization of llms should also be given attention. this is the reason why the focus of our work, the existence of gender bias in llm-generated contexts, is essential to be seen, to be understood, and to be addressed step by step in future developments.