Comparative Performance of Multilingual versus English-Only Intermediate Task Fine-Tuning 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 performance of multilingual intermediate task fine-tuning compare to English-only intermediate task fine-tuning on the XTREME-R benchmark when controlling for model size and training duration?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.