Leveraging In-Context Tuning for Efficient One-Shot Cross-Lingual Text Classification

The proposed In-Context Cross-Lingual Transfer (IC-XLT) approach effectively leverages target-language demonstrations during inference to improve cross-lingual text classification performance, especially in scenarios with limited source-language data.