Large Language Models (LLMs) perform complex reasoning by generating
explanations for their predictions. However, a complementary goal of
explanations is to also communicate useful knowledge that improves weaker
agents. Hence, we investigate whether LLMs also make good teachers for weaker
agents. In particular, we consider a student-teacher framework between two LLM
agents and study if, when, and how the teacher should intervene with natural
language explanations to improve the student's performance. Since communication
is expensive, we define a budget such that the teacher only communicates
explanations for a fraction of the data, after which the student should perform
well on its own. We decompose the teaching problem along four axes: (1) if
teacher's test time intervention improve student predictions, (2) when it is
worth explaining a data point, (3) how the teacher should personalize
explanations to better teach the student, and (4) if teacher explanations also
improve student performance on future unexplained data. We first show that
teacher LLMs can indeed intervene on student reasoning to improve their
performance. Next, we propose a Theory of Mind approach, in which the teacher
builds two few-shot mental models of the student. The first model defines an
Intervention Function that simulates the utility of an intervention, allowing
the teacher to intervene when this utility is the highest and improving student
performance at lower budgets. The second model enables the teacher to
personalize explanations for a particular student and outperform unpersonalized
teachers. We also demonstrate that in multi-turn interactions, teacher
explanations generalize and learning from explained data improves student
performance on future unexplained data. Finally, we also verify that misaligned
teachers can lower student performance to random chance by intentionally
misleading them.

本文研究了大型语言模型在学生 - 教师框架中作为教师的潜力，探讨了教师在测试时干预学生推理和在特定学生个体上进行个性化解释的方法。