🎉 EMNLP 2025 Workshop Accepted

ThaiFACTUAL

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

A lightweight, model-agnostic framework that mitigates sentiment leakage and political entity bias in Thai political stance detection.

270

Annotated Samples

3

Political Figures

73.5

Macro-F1

55%

Bias Reduction

Overview

Thai political discourse presents unique challenges for LLMs due to sentiment-stance entanglement, indirect language, and entity preference bias. ThaiFACTUAL introduces counterfactual calibration to disentangle political stance from emotional tone.

Method

Tweet
↓
LLM Prediction
↓
Counterfactual Swap
↓
Rationale Calibration
↓
Debiased Stance

Bias Demonstration

❌ Raw LLM

Positive sentiment → Support

Negative sentiment → Against

Political entity strongly influences prediction.

✅ ThaiFACTUAL

Sentiment separated from stance.

Counterfactual reasoning applied.

More consistent and fair predictions.

Results

Model Bias ↓ F1 ↑ OOD ↑
GPT-4 21.7 70.8 56.4
GPT-4 Debias Prompt 18.3 71.9 57.0
LLaMA-3 CoT 16.5 68.1 59.7
ThaiFACTUAL 9.8 73.5 65.2

Paper & Citation

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

@inproceedings{panboonyuen2025thaifactual,
 title={ThaiFACTUAL},
 author={Teerapong Panboonyuen},
 booktitle={EMNLP 2025 Workshop}
}

Author

Teerapong Panboonyuen

Chulalongkorn University

MARSAIL Laboratory