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.

翻译：日益庞大的语言模型凭借不断增长的能力已成文本处理的标准工具。然而，命名实体识别等信息抽取任务仍主要依赖前代仅编码器结构的Transformer模型，尚未充分受益于此类技术进展。为此，我们提出一种简洁而有效的方法——信息引导的命名实体识别解码（iNERD），将命名实体识别视为生成过程。该方法以面向未来的方式利用当代生成式模型的语言理解能力，采用受信息引导的解码方案，将信息抽取的约束性质融入开放式文本生成，从而提升性能并彻底消除幻觉风险。我们通过合并命名实体语料库对模型进行粗调以增强性能，在八个命名实体识别数据集上评估了五种生成式语言模型，尤其在未知实体类别集合环境下取得了显著成果，充分证明了该方法的适应性。