LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty
WWW 2024(2024)
摘要
Named Entity Recognition (NER) serves as a fundamental task in natural
language understanding, bearing direct implications for web content analysis,
search engines, and information retrieval systems. Fine-tuned NER models
exhibit satisfactory performance on standard NER benchmarks. However, due to
limited fine-tuning data and lack of knowledge, it performs poorly on unseen
entity recognition. As a result, the usability and reliability of NER models in
web-related applications are compromised. Instead, Large Language Models (LLMs)
like GPT-4 possess extensive external knowledge, but research indicates that
they lack specialty for NER tasks. Furthermore, non-public and large-scale
weights make tuning LLMs difficult. To address these challenges, we propose a
framework that combines small fine-tuned models with LLMs (LinkNER) and an
uncertainty-based linking strategy called RDC that enables fine-tuned models to
complement black-box LLMs, achieving better performance. We experiment with
both standard NER test sets and noisy social media datasets. LinkNER enhances
NER task performance, notably surpassing SOTA models in robustness tests. We
also quantitatively analyze the influence of key components like uncertainty
estimation methods, LLMs, and in-context learning on diverse NER tasks,
offering specific web-related recommendations.
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