Impact of Target Task Training Set Size on Zero-Shot Cross-Lingual Transfer in XTREME-R with Intermediate-Task Training

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: What is the impact of varying the size of the target task training set on zero-shot cross-lingual transfer performance in XTREME-R when intermediate-task training is applied, measured by accuracy and inference efficiency?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.7/10.