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PromptNER: Prompting For Named Entity Recognition

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arxiv 2305.15444 v2 pith:4LHUF46T submitted 2023-05-24 cs.CL cs.AIcs.LG

Dhananjay Ashok , Zachary C. Lipton This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords few-shotpromptnerentityabsolutedatasetimprovementmethodsdefinitions
verification ladder T0 review T1 audit T2 compute T3 formal
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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 provided entity type definitions. Remarkably, PromptNER achieves state-of-the-art performance on few-shot NER, achieving a 4% (absolute) improvement in F1 score on the ConLL dataset, a 9% (absolute) improvement on the GENIA dataset, and a 4% (absolute) improvement on the FewNERD dataset. PromptNER also moves the state of the art on Cross Domain NER, outperforming prior methods (including those not limited to the few-shot setting), setting a new mark on 3/5 CrossNER target domains, with an average F1 gain of 3%, despite using less than 2% of the available data.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 24 citations worldwide. Full citation record

  1. NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings

    cs.IR 2025-09 conditional novelty 6.0 of 10

    Mid-layer LLM value vectors, projected through a contrastively trained MLP, enable zero-shot retrieval of documents by ad-hoc entity type.

  2. GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A fully automated pipeline for generating annotation schemas, guidelines, and synthetic labeled examples from documents improves zero-shot NER after fine-tuning.

  3. Small Language Model Makes an Effective Long Text Extractor

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A smaller span-based NER model with a compressed plus-shaped attention mechanism extracts long entities from very long texts with less memory than prior span-based methods.

  4. Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI

    cs.HC 2024-12 conditional novelty 6.0 of 10

    An interview study with 21 Reddit users found that an imperfect AI disclosure detector can still help people reflect on privacy risks, but needs context-aware explanations and personalization.

  5. A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection

    cs.CL 2024-12 conditional novelty 6.0 of 10

    Large language models with in-context learning outperform fine-tuned BERT and RoBERTa for music entity detection in user-generated content, but their edge shrinks for entities not memorized during pre-training.

  6. SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings

    cs.CL 2025-08 conditional novelty 5.0 of 10

    A parameter-efficient adapter bridging Whisper and TinyLlama reports relative improvements in speech recognition, named entity recognition, and sentiment analysis on low-resource benchmarks.

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    A contextual bandit that chooses among five query-rewrite strategies, conditioned on 17 linguistic features, reduces LLM hallucination on QA benchmarks and beats static prompting and no-rewrite baselines.

  8. RetrieveAll: A Multilingual Named Entity Recognition Framework with Large Language Models

    cs.CL 2025-05 conditional novelty 5.0 of 10

    RetrieveAll combines per-language LoRA adapters with retrieval of entity and context examples to improve multilingual NER, claiming an average 12.1% F1 gain on PAN-X.

  9. Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph

    cs.CL 2024-11 reject novelty 5.0 of 10

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    EventHunter automatically clusters fragmented hacker-forum posts into security events and ranks them by timeliness, relevance, credibility, and completeness.

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