Self-Debias: Self-correcting for Debiasing Large Language Models

TL;DR

Self-Debias is a novel framework that enables large language models to self-correct social biases during reasoning, using a resource redistribution approach and online self-improvement with minimal supervision.

Contribution

It introduces a progressive, self-correcting debiasing method that reallocates probability mass and employs consistency filtering, reducing reliance on external interventions.

Findings

Achieves superior debiasing with only 20k annotated samples.

Preserves reasoning capabilities while reducing biases.

Enables autonomous self-correction during reasoning processes.

Abstract

Although Large Language Models (LLMs) demonstrate remarkable reasoning capabilities, inherent social biases often cascade throughout the Chain-of-Thought (CoT) process, leading to continuous "Bias Propagation". Existing debiasing methods primarily focus on static constraints or external interventions, failing to identify and interrupt this propagation once triggered. To address this limitation, we introduce Self-Debias, a progressive framework designed to instill intrinsic self-correction capabilities. Specifically, we reformulate the debiasing process as a strategic resource redistribution problem, treating the model's output probability mass as a limited resource to be reallocated from biased heuristics to unbiased reasoning paths. Unlike standard preference optimization which applies broad penalties, Self-Debias employs a fine-grained trajectory-level objective subject to dynamic debiasing constraints. This enables the model to selectively revise biased reasoning suffixes while preserving valid contextual prefixes. Furthermore, we integrate an online self-improvement mechanism utilizing consistency filtering to autonomously synthesize supervision signals. With merely 20k annotated samples, Self-Debias activates efficient self-correction, achieving superior debiasing performance while preserving general reasoning capabilities without continuous external oversight.