arXiv:2601.17898v2 Announce Type: replace Abstract: Named entity recognition (NER) is evolving from a sequence labeling task into a generative paradigm with the rise of large language models (LLMs). We conduct a systematic evaluation of open-source LLMs on both flat and nested NER tasks. We investigate several research questions including the performance gap between generative NER and traditional NER models, the impact of output formats, whether LLMs rely on memorization, and the preservation of general capabilities after fine-tuning. Through experiments across eight LLMs of varying scales and
The proliferation of powerful large language models is fundamentally altering established NLP paradigms, necessitating a re-evaluation of core tasks like Named Entity Recognition.
This shift indicates a move from specialized, engineered solutions to more generalist, emergent capabilities in AI, impacting the development and deployment of NLP systems.
The basic approach to Named Entity Recognition is evolving from a sequence labeling task to a generative one, potentially simplifying development but introducing new complexities like output format dependency.
- · LLM developers
- · companies building generative AI applications
- · researchers in generative AI
- · cloud providers
- · developers of traditional NER models
- · companies heavily invested in older NLP paradigms
Generative NER models become a standard component in AI applications, leading to more flexible and context-aware information extraction.
The demand for increasingly powerful and versatile LLMs will accelerate, driving further investment and innovation in foundation models.
The integration of generative NER in broader AI agent architectures could significantly enhance autonomous data processing and decision-making capabilities.
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