Summary of Laying the Foundation First? Investigating the Generalization From Atomic Skills to Complex Reasoning Tasks, by Yuncheng Huang et al.
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks
by Yuncheng Huang, Qianyu He, Yipei Xu, Jiaqing Liang, Yanghua Xiao
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposed probing framework investigates whether language models’ atomic skills can spontaneously generalize to complex reasoning tasks, while the hierarchical curriculum learning strategy achieves better skill generalization. The approach significantly improves open-source language models’ performance on complex tasks and exhibits effectiveness in cross-dataset and cross-domain scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models have shown basic reasoning capabilities but struggle with more complicated tasks that require combining atomic skills like math word problems. Previous methods either didn’t improve model skills or didn’t attempt to generalize them to complex tasks. This paper proposes a new framework and training strategy to help language models reason better. The results show that language models can learn to solve complex problems by first learning basic skills, which is important for designing better training strategies. |
Keywords
* Artificial intelligence * Curriculum learning * Generalization
