用大模型生成个性化学习回答，提升互动效率
LLM-Driven Personalized Answer Generation and Evaluation
- 基于学习者提问，用大模型生成定制化答案
- 给示例能显著提升回答适配性，尤其在少样本场景
- 适合教育科技、智能辅导系统研发者参考
在线学习因灵活性和可及性而迅速发展。个性化教学——根据个体学习者需求调整内容——对提升学习体验至关重要，尤其是在在线环境中。本研究探索大型语言模型（LLMs）生成针对学习者问题的个性化回答的潜力，以增强参与度并减轻教师负担。我们基于StackExchange平台，在语言学习和编程两个领域开展综合研究，构建了验证自动个性化回答的框架与数据集。采用0-shot、1-shot及少样本策略生成答案，并通过三种方式评估：BERTScore、LLM评价与人工评估。结果表明，向大模型提供目标回答示例（来自该学习者或相似学习者）能显著提升其生成符合个体需求答案的能力。
原文摘要 · Abstract (English)
Online learning has experienced rapid growth due to its flexibility and accessibility. Personalization, adapted to the needs of individual learners, is crucial for enhancing the learning experience, particularly in online settings. A key aspect of personalization is providing learners with answers customized to their specific questions. This paper therefore explores the potential of Large Language Models (LLMs) to generate personalized answers to learners' questions, thereby enhancing engagement and reducing the workload on educators. To evaluate the effectiveness of LLMs in this context, we conducted a comprehensive study using the StackExchange platform in two distinct areas: language learning and programming. We developed a framework and a dataset for validating automatically generated personalized answers. Subsequently, we generated personalized answers using different strategies, including 0-shot, 1-shot, and few-shot scenarios. The generated answers were evaluated using three methods: 1. BERTScore, 2. LLM evaluation, and 3. human evaluation. Our findings indicated that providing LLMs with examples of desired answers (from the learner or similar learners) can significantly enhance the LLMs' ability to tailor responses to individual learners' needs.
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