LLMs for Pivotal Sentence Detection in Learners’ Chinese Leveraging Industrial IoT Data Streams

This study presents a novel application of Large Language Models (LLMs) for detecting and analyzing pivotal sentence errors in Chinese as a Second Language (CSL) learners’ writing. Pivotal sentences, characterized by a verb’s object functioning as the subsequent verb’s subject, are a challenging syntactic structure for CSL learners. We propose a multi-agent framework comprising a syntax analysis agent, a semantic role labeling agent, an error diagnosis agent, and a decision agent to evaluate the grammatical correctness of pivotal sentences. Additionally, a reflective mechanism generates clear and pedagogically valuable error explanations. Experiments conducted on a large corpus of learner Chinese demonstrate the effectiveness of our LLM-based approach in achieving high accuracy in error detection and providing insightful feedback, establishing a robust foundation for intelligent CSL education focused on complex syntactic structures.