In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs). The main cause of high latency in LLMs is the sequential decoding process, which autoregressively generates all labels and mentions for NER, significantly increase the sequence length. To this end, we introduce Parallel Decoding in LLM for NE} (PaDeLLM-NER), a approach that integrates seamlessly into existing generative model frameworks without necessitating additional modules or architectural modifications. PaDeLLM-NER allows for the simultaneous decoding of all mentions, thereby reducing generation latency. Experiments reveal that PaDeLLM-NER significantly increases inference speed that is 1.76 to 10.22 times faster than the autoregressive approach for both English and Chinese. Simultaneously it maintains the quality of predictions as evidenced by the performance that is on par with the state-of-the-art across various datasets.

翻译：本研究旨在降低使用大语言模型（LLMs）进行命名实体识别（NER）时的生成延迟。LLMs高延迟的主要原因是其顺序解码过程——该过程通过自回归方式生成NER任务中的所有标签和提及项，导致序列长度显著增加。为此，我们提出了PaDeLLM-NER（面向NER的大语言模型并行解码方法），该方法可无缝集成至现有生成模型框架中，无需额外模块或架构调整。PaDeLLM-NER支持同时解码所有提及项，从而降低了生成延迟。实验表明，在英文和中文任务中，PaDeLLM-NER的推理速度较自回归方法提升1.76至10.22倍，同时保持预测质量——其在多个数据集上的性能与当前最先进方法持平。