Comparative Efficiency of Intermediate-Task Training Versus Direct Fine-Tuning for Cross-Lingual Models on PAWS-X

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 goal: How does the efficiency of intermediate-task training (measured by inference speed and memory usage) compare to direct fine-tuning on target tasks for cross-lingual models when evaluated on PAWS-X?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.