PromptNER: Prompting For Named Entity Recognition
Dhananjay Ashok, Zachary C. Lipton

TL;DR
PromptNER leverages prompt-based techniques with large language models to achieve state-of-the-art results in few-shot and cross-domain Named Entity Recognition, significantly outperforming existing methods.
Contribution
Introduces PromptNER, a novel prompt-based algorithm that enhances few-shot and cross-domain NER performance using entity definitions and LLM-generated explanations.
Findings
Achieves 4-9% absolute F1 score improvements on standard NER datasets.
Sets new benchmarks on 3 out of 5 cross-domain NER tasks.
Operates effectively with less than 2% of the data used by previous methods.
Abstract
In a surprising turn, Large Language Models (LLMs) together with a growing arsenal of prompt-based heuristics now offer powerful off-the-shelf approaches providing few-shot solutions to myriad classic NLP problems. However, despite promising early results, these LLM-based few-shot methods remain far from the state of the art in Named Entity Recognition (NER), where prevailing methods include learning representations via end-to-end structural understanding and fine-tuning on standard labeled corpora. In this paper, we introduce PromptNER, a new state-of-the-art algorithm for few-Shot and cross-domain NER. To adapt to any new NER task PromptNER requires a set of entity definitions in addition to the standard few-shot examples. Given a sentence, PromptNER prompts an LLM to produce a list of potential entities along with corresponding explanations justifying their compatibility with the…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Data Quality and Management
