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This paper presents a multitask GLiNER-based framework for scalable monitoring of dataset usage in research literature, using synthetic data generation and LLM-based revalidation to address challenges in extraction, relation identification, and usage classification.
GLiClass adapts GLiNER for efficient sequence classification with zero-shot and few-shot capabilities, and incorporates PPO for multi-label text classification in data-sparse conditions.