Performance Comparison of Intermediate-Task Training on Domain-Specific vs. General Language Understanding Tasks in Zero-Shot

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

How does the performance of intermediate-task training on domain-specific tasks (e.g., legal, medical) compare to general language understanding tasks when evaluated on zero-shot cross-lingual transfer in XTREME-R?

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