A Heuristic Perspective on Debiasing Language Models

2026-08-01

AI Summary

This work addresses the challenge that language models often acquire societal biases during pretraining, while existing debiasing methods typically rely on costly human annotations, incur high computational overhead, and struggle to generalize across cultures. To overcome these limitations, the authors propose the HEIMAT framework, which leverages heuristic templates to automatically generate prompts that surface biases and employs a context-guided fine-tuning strategy to minimize the Jensen–Shannon divergence between prediction distributions. This approach enables fully automated, annotation-free debiasing that is adaptable to diverse cultural contexts. Experimental results demonstrate that HEIMAT effectively mitigates model biases while preserving linguistic competence, substantially improving both debiasing efficiency and cross-cultural generalizability.

Abstract

Language models (LMs) often acquire various biases during pre-training and may express them in interactions, potentially causing social harm. Existing methods often rely on counterfactual augmentation or representation projection. These strategies remain limited in practice due to their high computational costs and difficulty in scaling to larger models. Additionally, many of these strategies require manual data annotation, narrowing their scope to specific cultures and bias categories. To overcome these limitations, we propose HEIMAT, a HEurIstic-style autoMATic debiasing framework for LMs. HEIMAT consists of two main steps: bias disclosure and debiasing fine-tuning. In the first step, it uses simple templates to construct heuristic prompts, which are applied to reveal model biases and generate corresponding context prompts. In the second step, it fine-tunes the model by minimizing the Jensen-Shannon divergence of predictions on these context prompts to reduce bias. Extensive experiments show that HEIMAT effectively mitigates bias in different cultures while maintaining the model's natural language understanding (NLU) performance.

Problem

• language models
• bias mitigation
• social harm
• computational cost
• manual annotation

Innovation

• heuristic debiasing
• automatic bias mitigation
• Jensen-Shannon divergence
• prompt-based probing
• language model fairness