Debiasing Large Language Models in Thai Political Stance Detection via Counterfactual Calibration
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909808634888192 |
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| author | Sermsri, Kasidit Panboonyuen, Teerapong |
| author_facet | Sermsri, Kasidit Panboonyuen, Teerapong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_21946 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Debiasing Large Language Models in Thai Political Stance Detection via Counterfactual Calibration Sermsri, Kasidit Panboonyuen, Teerapong Computation and Language Artificial Intelligence 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. |
| title | Debiasing Large Language Models in Thai Political Stance Detection via Counterfactual Calibration |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.21946 |