Ever-larger language models with ever-increasing capabilities are by now
well-established text processing tools. Alas, information extraction tasks such
as named entity recognition are still largely unaffected by this progress as
they are primarily based on the previous generation of encoder-only transformer
models. Here, we propose a simple yet effective approach, Informed Named Entity
Recognition Decoding (iNERD), which treats named entity recognition as a
generative process. It leverages the language understanding capabilities of
recent generative models in a future-proof manner and employs an informed
decoding scheme incorporating the restricted nature of information extraction
into open-ended text generation, improving performance and eliminating any risk
of hallucinations. We coarse-tune our model on a merged named entity corpus to
strengthen its performance, evaluate five generative language models on eight
named entity recognition datasets, and achieve remarkable results, especially
in an environment with an unknown entity class set, demonstrating the
adaptability of the approach.

提出了一种简单而有效的方法，Informed Named Entity Recognition Decoding (iNERD)，将命名实体识别视为一种生成过程，利用近期生成模型的语言理解能力，并采用了基于信息提取的有限文本生成的解码方案，以提高性能和消除幻觉风险，我们在合并的命名实体语料库上训练了模型，评估了五个生成语言模型在八个命名实体识别数据集上的表现，并取得了显著的结果，特别是在未知实体类别集的环境中，展示了该方法的适应性。