小模型也能赋能教育AI，打破大模型垄断
Small but Significant: On the Promise of Small Language Models for Accessible AIED
- 用小型语言模型替代大模型解决教育难题
- Phi-2在知识组件发现任务中表现接近大模型
- 适合资源有限的教育机构使用
GPT已成为大型语言模型（LLMs）的代名词，这在教育人工智能（AIED）领域愈发普遍。关键词搜索显示，AIED 2024年76篇长文和短文中，61%提出了基于LLMs的新解决方案，其中43%明确提及GPT。尽管以GPT为代表的大型语言模型为教育中的长期挑战带来新机遇，但其对高参数量（超过100亿）资源密集型模型的过度关注，可能忽视小型语言模型（SLMs）在为资源受限机构提供公平、低成本高质量AI工具方面的潜力。我们在知识组件（KC）发现这一关键教育挑战上验证了积极结果，表明像Phi-2这样的小模型无需复杂提示策略即可有效工作。因此，我们呼吁更多关注基于小模型的AIED方法开发。
原文摘要 · Abstract (English)
GPT has become nearly synonymous with large language models (LLMs), an increasingly popular term in AIED proceedings. A simple keyword-based search reveals that 61% of the 76 long and short papers presented at AIED 2024 describe novel solutions using LLMs to address some of the long-standing challenges in education, and 43% specifically mention GPT. Although LLMs pioneered by GPT create exciting opportunities to strengthen the impact of AI on education, we argue that the field's predominant focus on GPT and other resource-intensive LLMs (with more than 10B parameters) risks neglecting the potential impact that small language models (SLMs) can make in providing resource-constrained institutions with equitable and affordable access to high-quality AI tools. Supported by positive results on knowledge component (KC) discovery, a critical challenge in AIED, we demonstrate that SLMs such as Phi-2 can produce an effective solution without elaborate prompting strategies. Hence, we call for more attention to developing SLM-based AIED approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。