# Rethinking Negative Instances for Generative Named Entity Recognition

arXiv: https://arxiv.org/abs/2402.16602 · HTML: https://mlsanity.com/paper/2402.16602/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Named Entity Recognition as Dependency Parsing](https://mlsanity.com/paper/2005.07150/) (2020)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction](https://mlsanity.com/paper/2310.03668/) (2023)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [Unified Named Entity Recognition as Word-Word Relation Classification](https://mlsanity.com/paper/2112.10070/) (2021)
- [CrossWeigh: Training Named Entity Tagger from Imperfect Annotations](https://mlsanity.com/paper/1909.01441/) (2019)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition](https://mlsanity.com/paper/2012.05426/) (2020)
- [POLYGLOT-NER: Massive Multilingual Named Entity Recognition](https://mlsanity.com/paper/1410.3791/) (2014)
- [Coarse-to-Fine Pre-training for Named Entity Recognition](https://mlsanity.com/paper/2010.08210/) (2020)
- [Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts](https://mlsanity.com/paper/2210.03797/) (2022)
- [FindVehicle and VehicleFinder: A NER dataset for natural language-based vehicle retrieval and a keyword-based cross-modal vehicle retrieval system](https://mlsanity.com/paper/2304.10893/) (2023)
- [Rethinking Negative Sampling for Handling Missing Entity Annotations](https://mlsanity.com/paper/2108.11607/) (2021)

## Built on by (most cited first, 13 of 13)

- [LTNER: Large Language Model Tagging for Named Entity Recognition with Contextualized Entity Marking](https://mlsanity.com/paper/2404.05624/) (2024)
- [Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition](https://mlsanity.com/paper/2406.11192/) (2024)
- [Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER](https://mlsanity.com/paper/2407.01272/) (2024)
- [Entity Decomposition with Filtering: A Zero-Shot Clinical Named Entity Recognition Framework](https://mlsanity.com/paper/2407.04629/) (2024)
- [WhisperNER: Unified Open Named Entity and Speech Recognition](https://mlsanity.com/paper/2409.08107/) (2024)
- [KnowCoder-X: Boosting Multilingual Information Extraction via Code](https://mlsanity.com/paper/2411.04794/) (2024)
- [Can LLM Agents Sustain Long-Horizon Organizational Dynamics?](https://mlsanity.com/paper/2606.01199/) (2026)
- [Reliable Financial Named Entity Recognition Under Domain Shift: Confidence Estimation and Selective Prediction](https://mlsanity.com/paper/2608.19558/) (2026)
- [OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning](https://mlsanity.com/paper/2405.05957/) (2024)
- [Towards DS-NER: Unveiling and Addressing Latent Noise in Distant Annotations](https://mlsanity.com/paper/2505.12454/) (2025)
- [PANER: A Paraphrase-Augmented Framework for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2510.17720/) (2025)
- [DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition](https://mlsanity.com/paper/2604.15866/) (2026)
- [DIRECT: Direct Decoding for Efficient and Aligned Sequence Labeling with Large Language Models](https://mlsanity.com/paper/2607.26891/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema. In this work, we explore the potential enhancement of the existing methods by incorporating negative instances into training. Our experiments reveal that negative instances contribute to remarkable improvements by (1) introducing contextual information, and (2) clearly delineating label boundaries. Furthermore, we introduce an efficient longest common subsequence (LCS) matching algorithm, which is tailored to transform unstructured predictions into structured entities. By integrating these components, we present GNER, a Generative NER system that shows improved zero-shot performance across unseen entity domains. Our comprehensive evaluation illustrates our system's superiority, surpassing state-of-the-art (SoTA) methods by 9 F1 score in zero-shot evaluation.