  Teaching to improve student models (e.g., knowledge distillation) is an
extensively studied methodology in LLMs. However, for humans, teaching not only
improves students but also improves teachers. We ask: Can LLMs also learn by
teaching (LbT)? If yes, we can potentially unlock the possibility of
continuously advancing the models without solely relying on human-produced data
or stronger models. In this paper, we provide a preliminary exploration of this
ambitious agenda. We show that LbT ideas can be incorporated into existing LLM
training/prompting pipelines and provide noticeable improvements. Specifically,
we design three methods, each mimicking one of the three levels of LbT in
humans: observing students' feedback, learning from the feedback, and learning
iteratively, with the goals of improving answer accuracy without training and
improving models' inherent capability with fine-tuning. The findings are
encouraging. For example, similar to LbT in human, we see that: (1) LbT can
induce weak-to-strong generalization: strong models can improve themselves by
teaching other weak models; (2) Diversity in students might help: teaching
multiple students could be better than teaching one student or the teacher
itself. We hope that this early promise can inspire future research on LbT and
more broadly adopting the advanced techniques in education to improve LLMs. The
code is available at https://github.com/imagination-research/lbt.
