Debiasing Large Language Models in Thai Political Stance Detection via Counterfactual Calibration

• URL: http://arxiv.org/abs/2509.21946v1
• Date: Fri, 26 Sep 2025 06:26:21 GMT
• Title: Debiasing Large Language Models in Thai Political Stance Detection via Counterfactual Calibration
• Authors: Kasidit Sermsri, Teerapong Panboonyuen,
• Abstract summary: Thai politics is marked by indirect language, polarized figures, and entangled sentiment and stance.<n>Political stance detection in low-resource and culturally complex settings poses a critical challenge for large language models.<n>We present ThaiFACTUAL, a lightweight, model-agnostic calibration framework that mitigates political bias without requiring fine-tuning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Political stance detection in low-resource and culturally complex settings poses a critical challenge for large language models (LLMs). In the Thai political landscape - marked by indirect language, polarized figures, and entangled sentiment and stance - LLMs often display systematic biases such as sentiment leakage and favoritism toward entities. These biases undermine fairness and reliability. We present ThaiFACTUAL, a lightweight, model-agnostic calibration framework that mitigates political bias without requiring fine-tuning. ThaiFACTUAL uses counterfactual data augmentation and rationale-based supervision to disentangle sentiment from stance and reduce bias. We also release the first high-quality Thai political stance dataset, annotated with stance, sentiment, rationales, and bias markers across diverse entities and events. Experimental results show that ThaiFACTUAL significantly reduces spurious correlations, enhances zero-shot generalization, and improves fairness across multiple LLMs. This work highlights the importance of culturally grounded debiasing techniques for underrepresented languages.

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