Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes (2025)

Abstract: Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases. While recognition of these behaviors has generated an abundance of bias mitigation techniques, most require modifications to the training data, model parameters, or decoding strategy, which may be infeasible without access to a trainable model. In this work, we leverage the zero-shot capabilities of LLMs to reduce stereotyping in a technique we introduce as zero-shot self-debiasing. With two approaches, self-debiasing via explanation and self-debiasing via reprompting, we show that self-debiasing can significantly reduce the degree of stereotyping across nine different social groups while relying only on the LLM itself and a simple prompt, with explanations correctly identifying invalid assumptions and reprompting delivering the greatest reductions in bias. We hope this work opens inquiry into other zero-shot techniques for bias mitigation.

## introduction the rapid progress of large language models (llms) has ushered in a new era of technological capabilities, with increasing excitement around their few-and zero-shot capacities. for a wide range of tasks like question-answering and logical reasoning, simply modifying the prompting language can efficiently adapt the llm without finetuning (e.g., @xcite @xcite @xcite @xcite . while few-shot approaches condition the model on a few input-output exemplars, zero-shot learning adapts the model with no training data. at the same time as this success, however, llms have been shown to learn, reproduce, and even amplify denigrating, stereotypical, and exclusionary social behaviors (e.g., @xcite @xcite @xcite . we refer to this class of harms as "social bias," a normative term that characterizes disparate representations, treatments, or outcomes between social groups due to historical and structural power imbalances. the growing recognition of these harms has led to an abundance of works proposing bias mitigations for llms. one major drawback of many mitigation techniques, however, is their lack of scalability, computational feasibility, or generalization to different dimensions of bias. in contrast to existing bias mitigation approaches, downstream applications of llms often require more generalizable and efficient mitigations that can be easily applied to a black-box model with no information about the training data or model parameters. in this work, we introduce zero-shot selfdebiasing as an adaptation of zero-shot learning that leverages nothing other than the llm itself to elicit recognition and avoidance of stereotypes foot_0 in an llm. leveraging the bias benchmark for question answering @xcite , we demonstrate that simply asking the llm to explain potential stereotypes before answering, or prompting the llm to revise the answer with stereotypical behavior removed, can substantially decrease measured bias over nine diverse social groups. the reduction is statistically significant for all but two social groups for our explanation technique and all but one group for our reprompting technique. this paper makes two key contributions: (1) we introduce zero-shot self-debiasing as a promptingbased bias mitigation with two generalized approaches; and (2) we demonstrate self-debiasing's ability to decrease stereotyping in questionanswering over nine different social groups with a single prompt. ## related work the literature on bias mitigations for llms covers a broad range of pre-processing, in-training, and post-processing methods. many of these techniques, however, leverage augmented training data @xcite @xcite @xcite @xcite , additional fine-tuning @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite , modified decoding algorithms @xcite @xcite @xcite @xcite , or auxiliary post-processing models @xcite @xcite @xcite , which can be computationally expensive or require access to trainable model parameters, while often only addressing a single dimension of bias like gender or race. as part of the bias mitigation literature, @xcite first coined the term self-debiasing in a demonstration that llms can self-diagnose their biases. in contrast to this work, as well as most existing bias mitigation approaches, we focus instead on the llm's zero-shot capabilities as black-box models, without modification to the training data, parameters, or decoding algorithm. as such, our work follows more closely prompt and instructiontuning approaches for bias mitigation, which modify the prompting language to elicit a certain behavior from the model. because control tokens @xcite and continuous prompt tuning @xcite require additional fine-tuning, our work is most similar to techniques that prepend textual instructions or triggers to a prompt @xcite @xcite . similarly, some prompt tuning approaches add language to elicit reasoning in a versatile and scalable manner @xcite @xcite @xcite @xcite @xcite ; @xcite adapt this for harmful content. existing approaches, however, require careful prompt construction with somewhat limited success in reducing bias @xcite @xcite . we expand upon this work by simplifying the prompts while generalizing across a wide range of social groups. ## approach this section describes zero-shot self-debiasing to reduce stereotyping in llms. first, section 3.1 describes the dataset we use to operationalize stereotyping. next, section 3.2 describes the baseline approach. sections 3.3 and 3.4 introduce two self-debiasing techniques. finally, section 3.5 describes the experimental setup. ## results in this section, we discuss the results and findings. at a high level, we find that, regardless of the varying baseline levels of bias the llm exhibits for each social group, both self-debiasing techniques substantially reduce the degree of stereotyping. figure 1 shows the distribution of bootstrapped bias scores for the baseline, self-debiasing via explanation, and self-debiasing via reprompting approaches; see appendix c for extended results. sometimes, the llm will refuse to answer or will not answer with one of the multiple-choice options. when this occurs for any of the approaches, we drop the question from our analysis. the percentage of refusals for each social group is shown in ## conclusion we have introduced the framework of zero-shot self-debiasing as a bias reduction technique that relies only on an llm's own recognition of its potential stereotypes, and demonstrate two examplesself-debiasing via explanation and self-debiasing via reprompting-that both reduce bias across nine social groups and illustrate how to apply our method in the real world. explanations can correctly describe the mechanism of stereotyping, while reprompting is more token-efficient with even greater bias reductions. in short, simple, broad prompts can work across social groups to consistently reduce stereotyping. we hope this work encourages further exploration of zero-shot debiasing across different tasks, models, and settings.