Multimodal Intermediate-Task Training for Zero-Shot Cross-Lingual Transfer in XTREME-R Classification

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: Does intermediate-task training with multimodal benchmarks (e.g., LVLM, LLaVA) improve zero-shot cross-lingual transfer performance on XTREME-R classification tasks compared to text-only intermediate tasks, as measured by accuracy and robustness to domain shifts?

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