让AI像导师一样教学，提升学习效果
LearnLM: Improving Gemini for Learning
- 用教学指令引导训练，让模型学会按教学需求回应
- 在多种学习场景中，专家评分优于GPT-4o、Claude 3.5 Sonnet和原版Gemini 1.5 Pro
- 适用于教育场景开发者，可快速部署到Google AI Studio
当前生成式AI系统默认以呈现信息为目标，而非像人类导师般促进学习。为应对各类教育应用场景，本文将注入教学行为的挑战重构为「教学指令跟随」问题，即在训练与评估数据中加入描述具体教学属性的系统级指令。该方法不预设特定教学定义，允许教师或开发者灵活指定期望模型行为。同时，为提升Gemini模型的学习能力，可通过向后训练数据混合添加教学数据实现优化。相比初始技术报告，这是重要改进。实验表明，采用教学指令跟随训练的LearnLM模型（可在Google AI Studio获取）在多样学习场景中获得专家显著偏好：平均偏好强度较GPT-4o高出31%，较Claude 3.5 Sonnet高出11%，较其基础模型Gemini 1.5 Pro高出13%。
原文摘要 · Abstract (English)
Today's generative AI systems are tuned to present information by default, rather than engage users in service of learning as a human tutor would. To address the wide range of potential education use cases for these systems, we reframe the challenge of injecting pedagogical behavior as one of \textit{pedagogical instruction following}, where training and evaluation examples include system-level instructions describing the specific pedagogy attributes present or desired in subsequent model turns. This framing avoids committing our models to any particular definition of pedagogy, and instead allows teachers or developers to specify desired model behavior. It also clears a path to improving Gemini models for learning -- by enabling the addition of our pedagogical data to post-training mixtures -- alongside their rapidly expanding set of capabilities. Both represent important changes from our initial tech report. We show how training with pedagogical instruction following produces a LearnLM model (available on Google AI Studio) that experts substantially prefer across a diverse set of learning scenarios, with average preference strengths of +31\% over GPT-4o, +11\% over Claude 3.5 Sonnet, and +13\% over the Gemini 1.5 Pro model on which LearnLM was based.
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