@inproceedings{zhuang-etal-2026-bnlp,
title = "{BNLP}: A Text Annotation Platform for Quality Control of {LLM}-Generated Annotations",
author = "Zhuang, Xinhao and
Tian, Qiongyu and
Chen, Yalin and
Xin, Tianle and
Fu, Yongyong and
Ling, Yunchao and
Zhang, Guoqing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1185/",
doi = "10.18653/v1/2026.findings-acl.1185",
pages = "23675--23684",
ISBN = "979-8-89176-395-1",
abstract = "High-quality annotated data is crucial for NLP, yet manual annotation is costly and difficult to scale in low-resource settings. Large Language Models (LLMs) have demonstrated strong zero-shot and few-shot generalization in NLP tasks, but existing annotation tools either lack LLM support or use LLMs only as one-off pre-annotation engines, without incorporating collaboration or quality control, compromising data reliability. We present BNLP, a text annotation platform that embeds LLM-assisted labeling into a quality-aware collaborative workflow. BNLP treats LLM outputs as intermediate, revisable states and integrates multi-role collaboration, iterative review cycles, and consistency analysis to enable continuous quality monitoring while preserving efficiency gains. BNLP also natively supports AI-ready formats such as Excel and JSON, ensuring seamless data flow from manual annotation to model training. Experiments show that BNLP reduces annotation time by 74.3{\%} and improves annotation quality by 11.6{\%} over purely manual annotation in LLM-assisted settings."
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<abstract>High-quality annotated data is crucial for NLP, yet manual annotation is costly and difficult to scale in low-resource settings. Large Language Models (LLMs) have demonstrated strong zero-shot and few-shot generalization in NLP tasks, but existing annotation tools either lack LLM support or use LLMs only as one-off pre-annotation engines, without incorporating collaboration or quality control, compromising data reliability. We present BNLP, a text annotation platform that embeds LLM-assisted labeling into a quality-aware collaborative workflow. BNLP treats LLM outputs as intermediate, revisable states and integrates multi-role collaboration, iterative review cycles, and consistency analysis to enable continuous quality monitoring while preserving efficiency gains. BNLP also natively supports AI-ready formats such as Excel and JSON, ensuring seamless data flow from manual annotation to model training. Experiments show that BNLP reduces annotation time by 74.3% and improves annotation quality by 11.6% over purely manual annotation in LLM-assisted settings.</abstract>
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%0 Conference Proceedings
%T BNLP: A Text Annotation Platform for Quality Control of LLM-Generated Annotations
%A Zhuang, Xinhao
%A Tian, Qiongyu
%A Chen, Yalin
%A Xin, Tianle
%A Fu, Yongyong
%A Ling, Yunchao
%A Zhang, Guoqing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhuang-etal-2026-bnlp
%X High-quality annotated data is crucial for NLP, yet manual annotation is costly and difficult to scale in low-resource settings. Large Language Models (LLMs) have demonstrated strong zero-shot and few-shot generalization in NLP tasks, but existing annotation tools either lack LLM support or use LLMs only as one-off pre-annotation engines, without incorporating collaboration or quality control, compromising data reliability. We present BNLP, a text annotation platform that embeds LLM-assisted labeling into a quality-aware collaborative workflow. BNLP treats LLM outputs as intermediate, revisable states and integrates multi-role collaboration, iterative review cycles, and consistency analysis to enable continuous quality monitoring while preserving efficiency gains. BNLP also natively supports AI-ready formats such as Excel and JSON, ensuring seamless data flow from manual annotation to model training. Experiments show that BNLP reduces annotation time by 74.3% and improves annotation quality by 11.6% over purely manual annotation in LLM-assisted settings.
%R 10.18653/v1/2026.findings-acl.1185
%U https://aclanthology.org/2026.findings-acl.1185/
%U https://doi.org/10.18653/v1/2026.findings-acl.1185
%P 23675-23684
Markdown (Informal)
[BNLP: A Text Annotation Platform for Quality Control of LLM-Generated Annotations](https://aclanthology.org/2026.findings-acl.1185/) (Zhuang et al., Findings 2026)
ACL
- Xinhao Zhuang, Qiongyu Tian, Yalin Chen, Tianle Xin, Yongyong Fu, Yunchao Ling, and Guoqing Zhang. 2026. BNLP: A Text Annotation Platform for Quality Control of LLM-Generated Annotations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23675–23684, San Diego, California, United States. Association for Computational Linguistics.