Summary of Revorder: a Novel Method For Enhanced Arithmetic in Language Models, by Si Shen et al.
RevOrder: A Novel Method for Enhanced Arithmetic in Language Models
by Si Shen, Peijun Shen, Danhao Zhu
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 RevOrder is a novel technique designed to improve arithmetic operations in large language models (LLMs) by reversing the output digits in addition, subtraction, and n-digit by 1-digit (nD by 1D) multiplication tasks. This approach significantly reduces the Count of Sequential Intermediate Digits (CSID), a new metric introduced to assess equation complexity, to O(1). Through comprehensive testing, RevOrder achieves perfect accuracy in basic arithmetic operations and substantially boosts LLM performance in division tasks, particularly with large numbers where traditional models struggle. Implementation is cost-effective for both training and inference phases. The technique is also applied to fine-tune the LLaMA2-7B model on the GSM8K math task, resulting in a considerable improvement, reducing equation calculation errors by 46% and increasing overall scores from 41.6 to 44.4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces RevOrder, a new way to make large language models better at doing arithmetic. It works by changing the order of the numbers when adding, subtracting, or multiplying. This makes it much faster and more accurate for big calculations. The researchers tested it and found that it’s perfect for simple math problems and really helps with harder division tasks, especially when dealing with very large numbers. They also used it to improve a model on a math test and saw a significant improvement, cutting errors by almost half. |
Keywords
* Artificial intelligence * Inference
