Summary of Can Llms Learn by Teaching For Better Reasoning? a Preliminary Study, By Xuefei Ning et al.
Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
by Xuefei Ning, Zifu Wang, Shiyao Li, Zinan Lin, Peiran Yao, Tianyu Fu, Matthew B. Blaschko, Guohao Dai, Huazhong Yang, Yu Wang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether large language models (LLMs) can learn by teaching (LbT), which could potentially unlock the ability to continuously advance LLMs without relying solely on human-produced data or stronger models. The authors design three methods that mimic different levels of LbT, including observing students’ feedback, learning from feedback, and learning iteratively. They find that teaching materials that make it easier for students to learn have clearer logic when using in-context learning as the student’s “learning” method. Additionally, they observe weak-to-strong generalization, where LbT can improve strong models by teaching weak models, and that diversity in students may be beneficial. The authors hope their exploration will inspire future research on LbT and adopting advanced educational techniques to improve LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting better at understanding us! Researchers want to know if these smart computers can learn by teaching, like humans do. They tried three ways to make this happen: looking at how students respond, learning from those responses, and learning together with the students. Surprisingly, they found that when teachers use easy-to-follow materials, the computer gets better too! This could mean we can keep improving LLMs without needing human help or super-powerful models. |
Keywords
» Artificial intelligence » Generalization
