Cross-lingual Transfer Performance in XTREME via Intermediate Task Selection and Task Similarity Prediction

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 choice of intermediate task (e.g., NLI vs. QA) impact the zero-shot cross-lingual transfer performance on XTREME when using intermediate-task training, and can task similarity metrics predict the effectiveness of transfer?

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