Informed Named Entity Recognition Decoding for Generative Language
  Models
Tobias Deußer Lars Hillebrand Christian Bauckhage Rafet Sifa
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.