LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty
This addresses reliability issues in web-related applications like content analysis and search engines, though it is incremental as it builds on existing NER and LLM methods.
The paper tackles the problem of poor performance of fine-tuned NER models on unseen entities by proposing LinkNER, a framework that combines small fine-tuned models with LLMs using an uncertainty-based linking strategy, achieving better performance and surpassing SOTA models in robustness tests.
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. Code is available at https://github.com/zhzhengit/LinkNER.