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 are utilized for creating attribute-rich
training data. Furthermore, we preemptively generate entity terms and then
develop NER context data around these entities, effectively bypassing the LLMs'
challenges with complex structures. Our experiments across both general and
niche domains reveal significant performance enhancements over conventional
data generation methods while being more cost-effective than existing
alternatives.

该研究论文探讨了一种创新、成本效益的策略，利用具有适度命名实体识别能力的大型语言模型（LLMs）生成优质的命名实体识别数据集，通过引导 LLMs 自我反思特定领域生成与领域相关的属性，并围绕这些实体构建上下文数据，从而绕过 LLMs 在复杂结构上的挑战。实验证明，相对于传统的数据生成方法，该方法在通用领域和利基领域都取得了显著的性能提升，并具有更好的成本效益。