title: Can LLMs Learn by Teaching? A Preliminary Study
summary: 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.
paper_authors._id: 667e2a17039ad1fcb8d2e79f
paper_authors.name: Xuefei Ning
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paper_authors.name: Shiyao Li
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paper_authors.fullname: Shiyao Li
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paper_authors.name: Zinan Lin
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paper_authors.fullname: Zinan Lin
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paper_authors.name: Peiran Yao
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paper_authors.fullname: Peiran Yao
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paper_authors.name: Tianyu Fu
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paper_authors.fullname: Tianyu Fu
paper_authors.type: user
paper_authors.user: fuvty
paper_authors._id: 667e2a17039ad1fcb8d2e7a5
paper_authors.name: Matthew B. Blaschko
paper_authors._id: 667e2a17039ad1fcb8d2e7a6
paper_authors.name: Guohao Dai
paper_authors._id: 667e2a17039ad1fcb8d2e7a7
paper_authors.name: Huazhong Yang
paper_authors._id: 667e2a17039ad1fcb8d2e7a8
paper_authors.name: Yu Wang
paper_title: Can LLMs Learn by Teaching? A Preliminary Study
paper_summary: 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.
paper_ai_keywords: Teaching to improve student models Knowledge distillation LLMs (Large Language Models) Learning by Teaching (LbT) Student models Teacher models Training pipelines Prompting pipelines Observing students' feedback Learning from the feedback Learning iteratively Answer bounds Fine-tuning Weak-to-strong generalization