CHB ias: Bias Evaluation and Mitigation of C hinese Conversational Language Models (2023)

Abstract: Pretrained conversational agents have been exposed to safety issues, exhibiting a range of stereotypical human biases such as gender bias. However, there are still limited bias categories in current research, and most of them only focus on English. In this paper, we introduce a new Chinese dataset, CHBias, for bias evaluation and mitigation of Chinese conversational language models.Apart from those previous well-explored bias categories, CHBias includes under-explored bias categories, such as ageism and appearance biases, which received less attention. We evaluate two popular pretrained Chinese conversational models, CDial-GPT and EVA2.0, using CHBias. Furthermore, to mitigate different biases, we apply several debiasing methods to the Chinese pretrained models. Experimental results show that these Chinese pretrained models are potentially risky for generating texts that contain social biases, and debiasing methods using the proposed dataset can make response generation less biased while preserving the models’ conversational capabilities.

## introduction the success of the pretrained dialogue models benefits from the increasing quantity and quality of real corpora @xcite @xcite . however, deep neural models can inadvertently learn undesired features in the corpora, such as social biases. for example, @xcite shows that when gpt-3 @xcite encounters unsafe, harmful, and biased prompts related to some demographic groups, such as "old people" or "female", it may come up with biased replies. therefore, further progress is required on responsible and safe ai before applying these large language models in * equal contribution. addressing social biases in language generation models is still very challenging. a growing amount of work @xcite @xcite has started to study biases in language generation models. however, most of them @xcite either study one or two bias categories (e.g., gender bias and racial bias) or build artificial data for mitigating biases. more recent work, red-ditbias @xcite , extends bias categories to race, orientation, and religion. however, there are still other bias categories that are underexplored, for example, appearance bias and age bias. it is necessary to see whether the pretrained models are suffering from other new biases. moreover, existing works @xcite @xcite only focus on english dialogue models. however, the forms and demographic groups of bias may vary across languages due to differences in syntax, semantics, and cultural backgrounds. therefore, it is necessary to study the bias of non-english pretrained models. to better understand more bias categories for chinese in pretrained dialogue models, we introduce a new dataset named chbias, which is a chinese corpus for social bias evaluation and mitigation of chinese conversational models. chbias is based on data from weibo foot_0 and manually annotated for multiple social bias categories. it contains four social bias categories, including gender, orientation, age, and appearance, among which age and appearance are new categories provided by our chbias. based on the proposed chbias, we evaluate two state-of-the-art popular chinese pretrained dialogue models, cdial-gpt @xcite and eva2.0 @xcite . we show that responses generated by these chinese pretrained dialogue models suffer from different social biases. furthermore, to mitigate these biases in responses, we apply several mitigation methods to these dialogue models, including regularization-based debiasing methods and data augmentation-based methods using our chbias. we find that the debiasing methods can effectively reduce biases while maintaining the models' performance on dialogue tasks. our main contributions include: to the best of our knowledge, this is the first study to apply debiasing methods to chinese pretrained models. ## related work pretraind models pretrained models (bert @xcite , gpt @xcite , gpt-2 @xcite ) achieves great success on various language generation tasks. these pretrained models can be easily fine-tuned to be applied in different dialogue scenarios. dialogpt @xcite proposes a large-scale, tunable dialogue response generation model, which trains gpt-2 on 147m reddit foot_1 conversations. many previous works are mainly focused on english, but there also are some works @xcite that proposed pretrained dialogue generation model for chinese. cdial-gpt @xcite pretrained the chinese dialogue generation model on a chinese novel dataset, and they constructed the lccc dataset. eva2.0 @xcite is a chinese open-domain dialogue system based on large-scale pretraining. to ensure data quality and diversity, the training data are derived from the filtered wdc-dialogues @xcite dataset as well as publicly available datasets @xcite bias datasets since the real-world conversation data contains some biases, the models trained based on these data learn undesired features. more and more researchers @xcite are working to reduce the biases of pretrained models. ## bias evaluation we evaluate the bias of conversational models based on the following assumption: biased models tend to generate positive stereotype responses for one demographic group and negative stereotype responses for another demographic group. in the validation and test sets, there are biased examples from two demographic groups. their texts are the same except for the target terms. we compare the performance differences of the model across demographic groups to evaluate bias. we use the student's two-tailed test to calculate the difference between the perplexity distributions from a model for two demographic groups. first, we apply the pretrained model to the test data (two demographic groups) and calculate the perplexity scores @xcite for each demographic group. then we compare the distributions of perplexity to quantify the difference in model performance between the two groups. specifically, we use the "t-value" of the student's two-tailed test to compare the perplexity distributions among different demographic groups. the difference in perplexity distributions is used to quantify the bias of the model. each "t-value" corresponds to a "p-value", which is the probability that the sample data occurred by chance. the "t-value" is considered statistically significant if its corresponding "p-value" is within a given confidence interval (we set the α = 0.05 in this paper). the larger the difference in the model's performance on the demographic pairs, the more biased the model is towards these demographic groups, and the absolute value of the "t-value" will be larger as well. ## bias mitigation we evaluate the debiasing performance of five different methods (see section 5.3), including three loss-based methods: language model debiasing @xcite , attribute distance debiasing @xcite , and hard debiasing @xcite , as well as two data augmentation-based methods: counter attribute data augmentation and counter target data augmentation @xcite @xcite . we also conduct experiments to test whether these debiasing methods have any negative impact on the dialogue performance of the model (see section 5.3.2). furthermore, we implement human evaluation experiments to evaluate the effectiveness of the debiasing methods (see section 5.4). ## conclusion and discussion in this paper, we focus on bias evaluation and mitigation in chinese conversational models. we have proposed a new chinese dataset named chbias which contains four bias categories and is the first dataset for bias evaluation and mitigation of chinese pretrained models. through our proposed datasets, we evaluated pairs of state-of-the-art pretrained conversational models for chinese and found these pretrained models exhibit various biases. furthermore, we applied loss-based and dataaugmented debiasing methods to reduce the biases in the pretrained models. the results indicate that these debiasing methods can not only reduce the biases but also preserve the dialogue performance of the models. growing numbers of large language models (llms), such as gpt-3 @xcite and chatgpt 8 , are being proposed and achieving good performance in natural language processing (nlp) for many tasks. typically functioning as black boxes, these llms restrict user access to intermediate outputs, thereby preventing the utilization of our dataset for measuring model bias. however, our dataset and evaluation methods can assist developers of llms in detecting and mitigating the bias of their models.