Summary of Improve Student’s Reasoning Generalizability Through Cascading Decomposed Cots Distillation, by Chengwei Dai et al.
Improve Student’s Reasoning Generalizability through Cascading Decomposed CoTs Distillation
by Chengwei Dai, Kun Li, Wei Zhou, Songlin Hu
First submitted to arxiv on: 30 May 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 proposes a new method called Cascading Decomposed CoTs Distillation (CasCoD) to improve the reasoning capabilities of large language models. The authors argue that previous methods, which fine-tune student models on Chain-of-Thoughts (CoTs) data generated by teachers, struggle to generalize out-of-domain tasks due to spurious correlations between questions and answers. CasCoD addresses this issue by decomposing the training process into two cascaded learning steps, where students focus on learning rationales without interference from preset answers. The method demonstrates effectiveness on both in-domain and out-of-domain benchmark reasoning datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving how big language models reason. Right now, these models can do well with the data they’re trained on, but they struggle when faced with new problems that are different from what they’ve seen before. The authors think this is because the models are too focused on finding specific answers rather than understanding why those answers make sense. They propose a new way of training these models to help them focus more on learning reasons and less on finding specific answers. This new method, called CasCoD, shows promise in helping language models generalize better to new problems. |
Keywords
» Artificial intelligence » Distillation
