DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition

Hyung-Jin Yoon; Siun Kim

arxiv: 2604.15866 · v1 · submitted 2026-04-17 · 💻 cs.CL · cs.AI· cs.LG

DiZiNER: Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition

Siun Kim , Hyung-Jin Yoon This is my paper

Pith reviewed 2026-05-10 08:42 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG

keywords zero-shot named entity recognitioninstruction refinementLLM disagreementspilot annotationmulti-model supervisioninformation extractionbenchmark resultsself-improvement

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The pith

DiZiNER refines zero-shot named entity recognition instructions by simulating pilot annotation disagreements among multiple LLMs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to close the performance gap in zero-shot named entity recognition by treating inconsistencies in LLM outputs as opportunities for instruction improvement. It draws a parallel to human annotators who clarify guidelines after seeing disagreements in a pilot round. The DiZiNER framework has several LLMs label the same texts, then has a supervisor model review their differences to produce better instructions. These improved instructions are used for the final zero-shot predictions. If effective, this would mean practitioners can enhance LLM performance on extraction tasks simply by leveraging model diversity rather than collecting new labeled examples or fine-tuning.

Core claim

DiZiNER simulates the pilot annotation process for zero-shot NER by having multiple heterogeneous LLMs act as annotators on shared texts and a supervisor model analyze their disagreements to refine the task instructions. This yields zero-shot state-of-the-art results on 14 of 18 benchmarks, with an average improvement of +8.0 F1 over prior methods and a reduction of the gap to supervised performance by more than 11 points. The refined system also beats the supervisor model GPT-5 mini on the tasks, and pairwise agreement among models correlates positively with NER performance.

What carries the argument

Disagreement-guided instruction refinement, in which a supervisor LLM examines differing annotations from multiple models to update the instructions.

Load-bearing premise

Disagreements between outputs of different LLMs reliably highlight instruction deficiencies that, when fixed, improve entity recognition across varied datasets.

What would settle it

If experiments on held-out NER datasets show that instructions refined via LLM disagreement analysis do not lead to higher F1 scores than the original instructions, the approach would be falsified.

Figures

Figures reproduced from arXiv: 2604.15866 by Hyung-Jin Yoon, Siun Kim.

**Figure 2.** Figure 2: Correlation between pairwise agreement and NER performance across training iterations.Each subplot [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

**Figure 3.** Figure 3: Evolution of NER performance and inter-annotator agreement across iterations for 18 benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

read the original abstract

Large language models (LLMs) have advanced information extraction (IE) by enabling zero-shot and few-shot named entity recognition (NER), yet their generative outputs still show persistent and systematic errors. Despite progress through instruction fine-tuning, zero-shot NER still lags far behind supervised systems. These recurring errors mirror inconsistencies observed in early-stage human annotation processes that resolve disagreements through pilot annotation. Motivated by this analogy, we introduce DiZiNER (Disagreement-guided Instruction Refinement via Pilot Annotation Simulation for Zero-shot Named Entity Recognition), a framework that simulates the pilot annotation process, employing LLMs to act as both annotators and supervisors. Multiple heterogeneous LLMs annotate shared texts, and a supervisor model analyzes inter-model disagreements to refine task instructions. Across 18 benchmarks, DiZiNER achieves zero-shot SOTA results on 14 datasets, improving prior bests by +8.0 F1 and reducing the zero-shot to supervised gap by over +11 points. It also consistently outperforms its supervisor, GPT-5 mini, indicating that improvements stem from disagreement-guided instruction refinement rather than model capacity. Pairwise agreement between models shows a strong correlation with NER performance, further supporting this finding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

DiZiNER uses multi-LLM disagreement to refine zero-shot NER instructions and reports solid benchmark gains, but lacks the control needed to credit the disagreement step itself.

read the letter

The paper's main contribution is a concrete procedure for zero-shot NER: run several heterogeneous LLMs on the same unlabeled texts, have a supervisor LLM examine where they disagree, and use that analysis to rewrite the task instructions. They evaluate on 18 standard benchmarks and report zero-shot SOTA on 14 of them, with roughly +8 F1 over previous best zero-shot numbers and a claimed reduction of more than 11 points in the gap to supervised performance. They also note that the method beats its own supervisor (GPT-5 mini), which they interpret as evidence that the refinement adds value beyond model scale. A correlation between pairwise model agreement and final F1 is presented as further support.

Referee Report

2 major / 2 minor

Summary. The paper introduces DiZiNER, a framework for zero-shot NER that simulates human pilot annotation by having multiple heterogeneous LLMs annotate the same texts and then using a supervisor LLM (GPT-5 mini) to analyze inter-model disagreements and refine the task instructions. It claims this yields zero-shot SOTA results on 14 of 18 benchmarks (+8.0 F1 average improvement over prior bests), reduces the zero-shot-to-supervised gap by >11 points, consistently beats the supervisor model, and shows strong correlation between pairwise model agreement and final NER F1.

Significance. If the central empirical claims hold after proper controls and details are supplied, the work would be significant for zero-shot IE: it offers a training-free, disagreement-driven instruction refinement procedure that appears to improve over both prior zero-shot methods and the supervisor LLM itself, with a plausible human-annotation analogy. The reported correlation between agreement and performance, if causally supported, could inform broader LLM consistency research.

major comments (2)

The central claim attributes the +8 F1 gains and outperformance of GPT-5 mini specifically to disagreement-guided refinement, yet the manuscript supplies no ablation in which the supervisor receives the same pilot texts and original instructions but is prompted to refine without seeing or referencing the inter-model disagreement annotations (e.g., a generic refinement prompt). Without this control, the observed correlation between pairwise agreement and NER F1 cannot establish that disagreement analysis is the causal mechanism rather than iterative supervisor prompting alone.
Abstract and results sections report quantitative gains across 18 benchmarks but provide no implementation details, ablation studies, statistical significance tests, error analysis, or exact model configurations. This absence prevents evaluation of whether the reported SOTA numbers are reproducible or robust.

minor comments (2)

Clarify the exact set of heterogeneous annotator LLMs, the precise disagreement quantification method, and the supervisor prompt template used for refinement.
The abstract refers to 'GPT-5 mini'; confirm the model identity and whether it is the same across all experiments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback, which identifies key areas where additional controls and transparency will strengthen the manuscript. We address each major comment below and will revise the paper accordingly.

read point-by-point responses

Referee: The central claim attributes the +8 F1 gains and outperformance of GPT-5 mini specifically to disagreement-guided refinement, yet the manuscript supplies no ablation in which the supervisor receives the same pilot texts and original instructions but is prompted to refine without seeing or referencing the inter-model disagreement annotations (e.g., a generic refinement prompt). Without this control, the observed correlation between pairwise agreement and NER F1 cannot establish that disagreement analysis is the causal mechanism rather than iterative supervisor prompting alone.

Authors: We agree that a dedicated ablation isolating disagreement analysis from generic iterative refinement by the supervisor is required to establish causality. While the manuscript shows DiZiNER outperforming the supervisor and a correlation between pairwise agreement and F1, these do not fully rule out benefits from repeated prompting alone. In the revision we will add the requested control: the supervisor will receive the same pilot texts and original instructions but will be prompted to refine without any reference to inter-model disagreements. We will report the results, compare them to the full DiZiNER pipeline, and update the causal claims and discussion as warranted. revision: yes
Referee: Abstract and results sections report quantitative gains across 18 benchmarks but provide no implementation details, ablation studies, statistical significance tests, error analysis, or exact model configurations. This absence prevents evaluation of whether the reported SOTA numbers are reproducible or robust.

Authors: We acknowledge that the current manuscript omits critical details needed for reproducibility and robustness evaluation. In the revised version we will expand the Experimental Setup and Results sections to include: exact model identifiers and hyperparameter settings for all LLMs (including GPT-5 mini temperature, top-p, and generation parameters), full prompt templates for annotation and supervision stages, complete ablation studies, statistical significance testing (bootstrap confidence intervals on F1 and paired tests for model comparisons), and a categorized error analysis of failure modes. We will also release code, prompts, and data splits to support independent reproduction. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical method with direct measurements

full rationale

The paper presents DiZiNER as an empirical framework that runs heterogeneous LLMs as annotators, has a supervisor analyze disagreements, and reports measured F1 scores on 18 fixed benchmarks. No equations, fitted parameters, or derivations appear in the provided text. Performance gains and outperformance of GPT-5 mini are stated as direct experimental outcomes, not quantities defined by construction from the method's own inputs. The central claim rests on benchmark results rather than any self-referential loop or renamed known result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work is empirical and introduces no free parameters, formal axioms beyond standard LLM usage, or new invented entities; the central claim rests on the effectiveness of disagreement analysis for instruction improvement.

axioms (1)

domain assumption Multiple heterogeneous LLMs can generate diverse annotations whose disagreements reveal deficiencies in task instructions.
This underpins the pilot annotation simulation and is invoked to justify the refinement step.

pith-pipeline@v0.9.0 · 5514 in / 1331 out tokens · 62187 ms · 2026-05-10T08:42:18.726464+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 5 canonical work pages

[1]

Bogdanov et al

Nuner: Entity recognition encoder pre- training via llm-annotated data.arXiv preprint arXiv:2402.15343. Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, and Le Sun. 2023. Learning in-context learn- ing for named entity recognition.arXiv preprint arXiv:2305.11038. Alexander Philip Dawid and Allan M Skene. 1979. ...

work page arXiv 2023
[2]

InProceedings of COLING 2016, the 26th international conference on compu- tational linguistics: Technical papers, pages 1169– 1179

Broad twitter corpus: A diverse named entity recognition resource. InProceedings of COLING 2016, the 26th international conference on compu- tational linguistics: Technical papers, pages 1169– 1179. Yuyang Ding, Juntao Li, Pinzheng Wang, Zecheng Tang, Bowen Yan, and Min Zhang. 2024. Rethinking nega- tive instances for generative named entity recognition. ...

work page arXiv 2016
[3]

InLinguistic Annotation Workshop, pages 142–145

Towards a methodology for named entities an- notation. InLinguistic Annotation Workshop, pages 142–145. Quanjiang Guo, Yihong Dong, Ling Tian, Zhao Kang, Yu Zhang, and Sijie Wang. 2024a. Baner: Boundary- aware llms for few-shot named entity recognition. arXiv preprint arXiv:2412.02228. Yucan Guo, Zixuan Li, Xiaolong Jin, Yantao Liu, Yu- tao Zeng, Wenxuan ...

work page arXiv 2013
[4]

arXiv preprint arXiv:2402.12801

Few-shot clinical entity recognition in english, french and spanish: masked language models out- perform generative model prompting.arXiv preprint arXiv:2402.12801. Chaoxu Pang, Yixuan Cao, Qiang Ding, and Ping Luo

work page arXiv
[5]

Common Instruction

Guideline learning for in-context information extraction.arXiv preprint arXiv:2310.05066. Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013. Towards robust linguistic analysis using ontonotes. InProceedings of the Seventeenth Conference on Computational Nat- ural Language Le...

work page arXiv 2013

[1] [1]

Bogdanov et al

Nuner: Entity recognition encoder pre- training via llm-annotated data.arXiv preprint arXiv:2402.15343. Jiawei Chen, Yaojie Lu, Hongyu Lin, Jie Lou, Wei Jia, Dai Dai, Hua Wu, Boxi Cao, Xianpei Han, and Le Sun. 2023. Learning in-context learn- ing for named entity recognition.arXiv preprint arXiv:2305.11038. Alexander Philip Dawid and Allan M Skene. 1979. ...

work page arXiv 2023

[2] [2]

InProceedings of COLING 2016, the 26th international conference on compu- tational linguistics: Technical papers, pages 1169– 1179

Broad twitter corpus: A diverse named entity recognition resource. InProceedings of COLING 2016, the 26th international conference on compu- tational linguistics: Technical papers, pages 1169– 1179. Yuyang Ding, Juntao Li, Pinzheng Wang, Zecheng Tang, Bowen Yan, and Min Zhang. 2024. Rethinking nega- tive instances for generative named entity recognition. ...

work page arXiv 2016

[3] [3]

InLinguistic Annotation Workshop, pages 142–145

Towards a methodology for named entities an- notation. InLinguistic Annotation Workshop, pages 142–145. Quanjiang Guo, Yihong Dong, Ling Tian, Zhao Kang, Yu Zhang, and Sijie Wang. 2024a. Baner: Boundary- aware llms for few-shot named entity recognition. arXiv preprint arXiv:2412.02228. Yucan Guo, Zixuan Li, Xiaolong Jin, Yantao Liu, Yu- tao Zeng, Wenxuan ...

work page arXiv 2013

[4] [4]

arXiv preprint arXiv:2402.12801

Few-shot clinical entity recognition in english, french and spanish: masked language models out- perform generative model prompting.arXiv preprint arXiv:2402.12801. Chaoxu Pang, Yixuan Cao, Qiang Ding, and Ping Luo

work page arXiv

[5] [5]

Common Instruction

Guideline learning for in-context information extraction.arXiv preprint arXiv:2310.05066. Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013. Towards robust linguistic analysis using ontonotes. InProceedings of the Seventeenth Conference on Computational Nat- ural Language Le...

work page arXiv 2013