Impact of Intermediate-Task Selection on Zero-Shot Cross-Lingual Transfer in LLMs for Logical Inference

Abstract

Abstract: Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effective fine-tuning strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a non-trivial task. In this paper, we propose a novel meta-optimizer to soft-select which layers of the pre-trained model to freeze during fine-tuning. We train the meta-optimizer by simulating the zero-shot transfer scenario. Results on cross-lingual natural language inference show that

Research Question

What is the impact of different intermediate-task selection strategies (e.g., task similarity, task difficulty) on the zero-shot cross-lingual transfer performance of LLMs for logical inference tasks?

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