Order of Intermediate Task Fine-Tuning and Cross-Lingual Transfer Performance on XTREME-R

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 order of intermediate task fine-tuning (e.g., cascading vs. parallel training) affect cross-lingual transfer performance on XTREME-R, measured by accuracy and F1 scores?

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