Robustness of XLM-R Models to Domain Shifts in Cross-Lingual Transfer Tasks

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 robust are XLM-R models fine-tuned with English intermediate-task training to domain shifts in cross-lingual transfer tasks, as measured by accuracy degradation on XTREME-R when evaluated on out-of-domain datasets?

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