Year: 2026
Type: conference-paper
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 LLMassisted 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 LLMassisted settings.1
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Chinese Academy of Sciences
Award: G3564989049
OpenAlex ID: W7166831667