Summary of Reverse That Number! Decoding Order Matters in Arithmetic Learning, by Daniel Zhang-li et al.
Reverse That Number! Decoding Order Matters in Arithmetic Learning
by Daniel Zhang-Li, Nianyi Lin, Jifan Yu, Zheyuan Zhang, Zijun Yao, Xiaokang Zhang, Lei Hou, Jing Zhang, Juanzi Li
First submitted to arxiv on: 9 Mar 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 presents a novel approach to teaching Large Language Models (LLMs) arithmetic operations, deviating from the traditional sequential methods. By prioritizing the output of the least significant digit and incorporating a step-by-step methodology, the authors achieve an overall improvement in accuracy while reducing complexity. The proposed method outperforms the state-of-the-art (SOTA) approach, requiring only one-third of the tokens typically used during training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do math! They’re really good at it too. But how they learn is important. Right now, most ways to teach them focus on doing each step in order. This paper tries something new: prioritizing the smallest part of the number and breaking it down into smaller steps. It works better than other methods that came before it, using fewer “building blocks” (called tokens) to train. |
