WATER: A Two-Stage in-Context Learning Debiasing Framework for Multilingual Text Classification

Recently, Large Language Models (LLMs) have shown remarkable success across a variety of tasks, with rapid advancements in supporting multilingual capabilities. However, these models exhibit varying degrees of demographic biases in text classification tasks. Most existing research focuses on debiasing pre-trained models or addressing biases in monolingual text classification, resulting in limited exploration in multilingual contexts. To solve the above problems, this paper introduces a tWo-stAge in-conText learning dEbiasing fRamework (WATER). Our approach does not require updating the model's parameters and is adaptable to any language. It includes three key modules: sample selection, sample filtering, and template filling and prediction. In the first stage, we leverage a sample selection module to identify text that closely matches the model embeddings. In the second stage, we introduce an innovative Contextual Disparity Measure (CDM) in the sample filtering module to filter out samples that effectively address the bias associated with specific attributes. Finally, the template filling and prediction module is used to fill the selected samples into the template and input them into the model to complete the multilingual text classification task. Our experimental results verify the effectiveness of our method in mitigating biases related to four sensitive attributes of gender, age, race, and country, demonstrating its potential to improve the fairness and accuracy of LLMs in multilingual classification tasks.