NeuralNexus 参与 BEA 2025 共享任务：面向AI导师错误识别的检索增强提示方法
NeuralNexus at BEA 2025 Shared Task: Retrieval-Augmented Prompting for Mistake Identification in AI Tutors
AI总结：
针对BEA 2025共享任务的AI导师数学错误识别赛道，团队探索四种方法后采用基于GPT 4o的检索增强少样本提示系统，其性能优于所有基线，验证了该组合方法在教学反馈评估中的有效性。
摘要：
本文介绍了我们为BEA 2025“AI导师教学能力评估”共享任务的赛道1：错误识别所开发的系统。该任务要求评估导师的回复是否正确识别出学生数学推理中的错误。我们探索了四种方法：（1）基于多个预训练语言模型（LM）的池化词元嵌入的机器学习模型集成；（2）搭配MLP分类器、使用<[BOS_never_used_51bce0c785ca2f68081bfa7d91973934]>嵌入的冻结sentence-transformer；（3）在词元级历史与回复嵌入间采用多头注意力的历史感知模型；（4）基于大语言模型（LLM）即GPT 4o的检索增强少样本提示系统。我们的最终系统检索语义相似的示例，构建结构化提示，并使用模式引导的输出解析生成可解释的预测。该系统性能优于所有基线，证明了将示例驱动提示与LLM推理相结合用于教学反馈评估的有效性。我们的代码可在https://github.com/NaumanNaeem/BEA_2025 获取。
英文摘要：
This paper presents our system for Track 1: Mistake Identification in the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors. The task involves evaluating whether a tutor's response correctly identifies a mistake in a student's mathematical reasoning. We explore four approaches: (1) an ensemble of machine learning models over pooled token embeddings from multiple pretrained language models (LMs); (2) a frozen sentence-transformer using [CLS] embeddings with an MLP classifier; (3) a history-aware model with multi-head attention between token-level history and response embeddings; and (4) a retrieval-augmented few-shot prompting system with a large language model (LLM) i.e. GPT 4o. Our final system retrieves semantically similar examples, constructs structured prompts, and uses schema-guided output parsing to produce interpretable predictions. It outperforms all baselines, demonstrating the effectiveness of combining example-driven prompting with LLM reasoning for pedagogical feedback assessment. Our code is available at https://github.com/NaumanNaeem/BEA_2025.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
