DiZiNER: Disagreement-guided Instruction Refinement via Simulating Pilot Annotation for Zero-shot Named Entity Recognition (2026.acl-long)
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| Challenge: | Large language models have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER) but their outputs still show persistent and systematic errors. |
| Approach: | They propose a framework that simulates the pilot annotation process and employs LLMs as both annotators and supervisors to refine model disagreements. |
| Outcome: | Using a pilot annotation process, the proposed framework outperforms its supervisor model on 18 benchmarks. |
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| Challenge: | Existing studies exploring the performance of large language models on named entity recognition tasks have focused on training task-specific LLMs for NER. |
| Approach: | They propose a training-free self-improving framework that utilizes an unlabeled corpus to stimulate the self-learning ability of LLMs. |
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A Herd of Language Models Makes a Better Zero-shot Annotator for Clinical Named Entity Recognition (2026.findings-acl)
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| Challenge: | Clinical named entity recognition (NER) is a core task in clinical NLP. |
| Approach: | They propose a label-modeling method for M**ulti-LLM **A**nnotation using **R**epresentation learning to capture contextual similarity. |
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ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions (2025.findings-acl)
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Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini
| Challenge: | Existing zero-shot learning methods rely on entity type names for generalization . current solutions require large datasets and prioritize a handful of commonly occurring types . |
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Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)
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| Challenge: | Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect. |
| Approach: | They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances. |
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| Challenge: | Named Entity Recognition (NER) is a low-resource task that requires supervised learning, but practical scenarios lack annotated data. |
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A Zero-shot and Few-shot Study of Instruction-Finetuned Large Language Models Applied to Clinical and Biomedical Tasks (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have enabled advances in the field of natural language processing . however, their application and potential are still underexplored . |
| Approach: | They evaluate four state-of-the-art instruction-tuned Large Language Models on 13 NLP tasks in English. |
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Scaling Performance and Low-Resource Annotation with Many-Shot In-Context Learning for Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing studies on ICL for Named Entity Recognition (NER) have mainly explored few-shot settings, but the potential of scaling to hundreds of demonstrations has not been thoroughly investigated. |
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| Challenge: | Existing language models can be refined for zero-shot commonsense reasoning . however, commons sense reasoning is still an unsolved problem . |
| Approach: | They propose a self-supervised learning approach that refines a pre-trained language model to boost conceptualization. |
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SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition (2026.findings-acl)
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| Challenge: | Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions misalign with a large language model’s intrinsic semantic organization. |
| Approach: | They propose a framework that stabilizes cross-domain transfer through an intermediate, domain-invariant archetype space. |
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NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
| Approach: | They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition . |
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