@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://preview.aclanthology.org/ingest-acl-workshops/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."
}