Intermediate-Task Training Robustness to Domain Divergence in Multilingual Cross-Lingual Transfer on XTREME

Abstract

Abstract: Transfer learning from large language models (LLMs) has emerged as a powerful technique to enable knowledge-based fine-tuning for a number of tasks, adaptation of models for different domains and even languages. However, it remains an open question, if and when transfer learning will work, i.e. leading to positive or negative transfer. In this paper, we analyze the knowledge transfer across three natural language processing (NLP) tasks - text classification, sentimental analysis, and sentence similarity, using three LLMs - BERT, RoBERTa, and XLNet - and analyzing their performance, by fine-tun

Research Question

Does the effectiveness of intermediate-task training for cross-lingual transfer on XTREME degrade when the intermediate task domain diverges significantly from the target task domain in multilingual settings?

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