Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models
This addresses the problem of reducing costly labeled data requirements for NER systems, making them more scalable and accessible, though it appears to be an incremental evaluation study.
This paper evaluates few-shot prompting with large language models for named entity recognition, finding that while there's a performance gap compared to fully supervised benchmarks, large models excel at adapting to new entity types and domains with minimal data.
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 effects of prompt engineering, guided output format and context length on performance. This study underscores Few-Shot Learning's potential to reduce the need for large labeled datasets, enhancing NER scalability and accessibility.