Dual-Contrastive Learning vs Prompt-Based Fine-Tuning for Few-Shot Cross-Lingual NER in Low-Resource Settings

An exciting advancement in the field of multilingual models is the emergence of autoregressive models with zero- and few-shot capabilities, a phenomenon widely reported in large-scale language models. To further improve model adaptation to cross-lingual tasks, another trend is to further fine-tune the language models with either full fine-tuning or parameter-efficient tuning. However, the interaction between parameter-efficient fine-tuning (PEFT) and cross-lingual tasks in multilingual autoregressive models has yet to be studied. Specifically, we lack an understanding of the role of linguistic

Research goal: How does dual-contrastive learning compare to prompt-based fine-tuning for few-shot cross-lingual NER in low-resource settings when evaluated on the XNLI benchmark?

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