# Intent-Aware Self-Correction for Mitigating Social Biases in Large Language Models Source: [Intent-Aware Self-Correction for Mitigating Social Biases in Large Language Models]() Collection index: [llms.txt]() Data updated: 2025-10-26T04:16:49.987Z Authors: Panatchakorn Anantaprayoon, Masahiro Kaneko, Naoaki Okazaki Published: 2025-03-08T02:20:43.000Z ## Abstract Self-Correction based on feedback improves the output quality of Large Language Models (LLMs). Moreover, as Self-Correction functions like the slow and conscious System-2 thinking from cognitive psychology's perspective, it can potentially reduce LLMs' social biases. LLMs are sensitive to contextual ambiguities and inconsistencies; therefore, explicitly communicating their intentions during interactions when applying Self-Correction for debiasing is crucial. In this study, we demonstrate that clarifying intentions is essential for effectively reducing biases in LLMs through Self-Correction. We divide the components needed for Self-Correction into three parts: instruction, response, and feedback, and clarify intentions at each component. We incorporate an explicit debiasing prompt to convey the intention of bias mitigation from the instruction for response generation. In the response, we use Chain-of-Thought (CoT) to clarify the reasoning process. In the feedback, we define evaluation aspects necessary for debiasing and propose clear feedback through multi-aspect critiques and scoring. Through experiments, we demonstrate that self-correcting CoT responses obtained from a debiasing prompt based on multi-aspect feedback can reduce biased responses more robustly and consistently than the baselines. We also find the variation in debiasing efficacy when using models with different bias levels or separating models for response and feedback generation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Question Answering on Fairness Evaluation Suite BBQ, CrP, GMO, SSt, WnQ](): Question Answering; Fairness Evaluation Suite BBQ, CrP, GMO, SSt, WnQ; BBQ Accuracy: 95.61. - [Fairness Evaluation on Fairness Evaluation Suite (BBQ, CrP, GMO, SSt, WnQ)](): Fairness evaluation; Fairness Evaluation Suite BBQ, CrP, GMO, SSt, WnQ; BBQ Score: 95.61. ## Original sources and implementation [Original paper]() [PDF]()