利用小型LLM进行教育领域的论证挖掘：论证成分识别、分类与评估
Leveraging Small LLMs for Argument Mining in Education: Argument Component Identification, Classification, and Assessment
AI总结：
针对教育领域论证挖掘中纯解码器模型未被充分探索的问题，提出利用开源小型LLMs通过小样本提示和微调完成论证成分分割、分类与质量评估，在相关数据集上验证了其在部分任务中优于基线的性能，凸显其教育应用潜力。
摘要：
论证挖掘算法可分析文章的论证结构，通过为学生的论证能力提供针对性反馈，成为提升教育质量的宝贵工具。当前方法常采用编码器或编码器-解码器深度学习架构，但纯解码器模型尚未得到充分探索，具有广阔的研究前景。本文提出通过小样本提示和微调，利用开源小型大型语言模型（LLMs）开展论证挖掘。这些模型的小尺寸和开源特性确保了可访问性、隐私性和计算效率，使学校和教育工作者能够在本地部署和使用。具体而言，我们开展了三项任务：将学生文章分割为论证成分、对论证成分进行类型分类、评估其质量。我们在Feedback Prize - Predicting Effective Arguments数据集（包含6-12年级学生文章）上对模型进行实证评估，结果表明微调后的小型LLMs在文章分割和论证类型判定任务上优于基线方法，而小样本提示在质量评估任务上的性能与基线相当。这项工作凸显了小型开源LLMs在教育领域的潜力，可提供实时个性化反馈，提升自主学习和写作技能，同时确保低计算成本和隐私保护。
英文摘要：
Argument mining algorithms analyze the argumentative structure of essays, making them a valuable tool for enhancing education by providing targeted feedback on the students' argumentation skills. While current methods often use encoder or encoder-decoder deep learning architectures, decoder-only models remain largely unexplored, offering a promising research direction. This paper proposes leveraging open-source, small Large Language Models (LLMs) for argument mining through few-shot prompting and fine-tuning. These models' small size and open-source nature ensure accessibility, privacy, and computational efficiency, enabling schools and educators to adopt and deploy them locally. Specifically, we perform three tasks: segmentation of student essays into arguments, classification of the arguments by type, and assessment of their quality. We empirically evaluate the models on the Feedback Prize - Predicting Effective Arguments dataset of grade 6-12 students essays and demonstrate how fine-tuned small LLMs outperform baseline methods in segmenting the essays and determining the argument types while few-shot prompting yields comparable performance to that of the baselines in assessing quality. This work highlights the educational potential of small, open-source LLMs to provide real-time, personalized feedback, enhancing independent learning and writing skills while ensuring low computational cost and privacy.
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。
