ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models

arXiv:2403.11103v1 Announce Type: cross
Abstract: Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER). This paper explores an innovative, cost-efficient strategy to harness LLMs with modest NER capabilities for producing superior NER datasets. Our approach diverges from the basic class-conditional prompts by instructing LLMs to self-reflect on the specific domain, thereby generating domain-relevant attributes (such as category and emotions for movie reviews), which …

abstract adaptability arxiv capabilities cost cs.cl cs.lg datasets domains extraction harness knowledge language language models large language large language models llms ner paper recognition step-by-step strategy tasks type