Performance comparison of cross-lingual NER models via teacher-student learning and few-shot LLMs on XTREME-R

Abstract

Abstract: This paper evaluates Few-Shot Prompting with Large Language Models for Named Entity Recognition (NER). Traditional NER systems rely on extensive labeled datasets, which are costly and time-consuming to obtain. Few-Shot Prompting or in-context learning enables models to recognize entities with minimal examples. We assess state-of-the-art models like GPT-4 in NER tasks, comparing their few-shot performance to fully supervised benchmarks. Results show that while there is a performance gap, large models excel in adapting to new entity types and domains with very limited data. We also explore the e

Research Question

How does the performance of cross-lingual NER models trained via teacher-student learning compare to few-shot prompting of large language models (LLMs) on the XTREME-R benchmark, measured by F1 score and inference latency?

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