Summary of Seq-vcr: Preventing Collapse in Intermediate Transformer Representations For Enhanced Reasoning, by Md Rifat Arefin et al.
Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
by Md Rifat Arefin, Gopeshh Subbaraj, Nicolas Gontier, Yann LeCun, Irina Rish, Ravid Shwartz-Ziv, Christopher Pal
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes Sequential Variance-Covariance Regularization (Seq-VCR), a method to improve the performance of Decoder-only Transformers in complex reasoning tasks, particularly arithmetic reasoning. The authors identify representation collapse as a key limitation and show that by enhancing the entropy of intermediate representations, they can prevent this collapse and achieve better results. Specifically, they use dummy pause tokens as substitutes for chain-of-thought (CoT) tokens, which leads to significant improvements in tasks like integer multiplication, arithmetic expression, and longest increasing subsequence. Compared to other models of similar size, their approach achieves much higher accuracy, even outperforming GPT-4 with CoT prompting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make machines better at solving math problems. Right now, these machines called Transformers are not very good at this because they can get stuck in a certain way of thinking. The authors found that by changing the way the machine thinks, they can make it much better at solving math problems. They tested their idea on some tricky math problems and it worked really well! In fact, it was even better than other machines that are supposed to be good at this kind of thing. |
Keywords
» Artificial intelligence » Decoder » Gpt » Prompting » Regularization
