Towards C ontrollable B iases in L anguage G eneration (2020)

Abstract: We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.

## introduction with the advent of more effective, large-scale natural language processing (nlp) techniques, the issue of fairness and bias is more important than ever. as such, there is an increasing repository of work dedicated to fairness in natural language processing. much of the work on social biases in natural language processing focuses on biases in word embeddings (bolukbasi et al., 2016; zhao et al., 2018b, 2019; kurita et al., 2019) and natural language understanding tasks (rudinger et al., 2018; zhao et al., 2018a; park et al., 2018). in contrast, there has been relatively little work on examining biases in natural language generation (nlg). however, biases in nlg applications ## problem definition and background given a pre-trained language generation model, our goal is to control the generation by inducing different bias polarities for the generated text when the model input contains mentions of specific demographics. we achieve this goal by formulating bias control objectives and adapting wallace et al. (2019)'s adversarial trigger search algorithm. once we find a suitable trigger, we prepend the trigger to model inputs to control generated outputs. ## controllable biases for nlg to control text generated from input prompts containing demographic mentions, we search for triggers that can induce targeted bias polarities in generated text. we first give an overview of the trigger search framework and define notations. next, we describe the two major components of our bias trigger search objective: bias association and dissociation, given specified demographic and target regard scores. finally, we build two concrete objectives for the trigger search algorithm: one to induce and one to mitigate biases. ## evaluation of bias triggers through automatic and human evaluations, we evaluate text generated using bias triggers and demonstrate the effectiveness of our proposed technique at inducing and mitigating biases. foot_7 ## bias triggers for dialogue generation since large-scale pre-trained language models such as gpt-2 are frequently used for downstream tasks, we examine how our techniques transfer to the nlg task of dialogue generation. we run our experiments on the pre-trained medium version of dialogpt (zhang et al., 2020). ## conclusion our framework for controllable biases in nlg can influence biases towards different demographic groups. we can gain more insight into an nlg model's learned biases by examining topics that correspond to demographic inequality in generated text and by comparing the effectiveness of bias triggers across demographics. bias triggers can also be used for mitigation, and our results indicate that these mitigation triggers are effective for both language and dialogue generation. future work includes investigating the generalizability of this framework to more variations in textual contexts.