Layer-wise adaptation in intermediate-task training for cross-lingual generalization in 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: What is the effect of layer-wise adaptation during intermediate-task training on the generalization performance of cross-lingual models across typologically diverse languages in XTREME-R?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.