Intermediate Task Diversity and Cross-Lingual Transfer Performance

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 intermediate task diversity (e.g., combining sentiment analysis and text classification) on cross-lingual transfer performance (measured by XTREME-R scores) when scaling intermediate-task training data size?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.