Summary of How Numerical Precision Affects Mathematical Reasoning Capabilities Of Llms, by Guhao Feng et al.
How Numerical Precision Affects Mathematical Reasoning Capabilities of LLMs
by Guhao Feng, Kai Yang, Yuntian Gu, Xinyue Ai, Shengjie Luo, Jiacheng Sun, Di He, Zhenguo Li, Liwei Wang
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 Transformers’ mathematical capabilities are crucial for their success in various domains. This paper delves into the theoretical analysis of Large Language Models (LLMs) to identify the key factors influencing their performance in arithmetic tasks. The results show that Transformers operating with low numerical precision struggle with tasks like iterated addition and integer multiplication, requiring super-polynomial model growth. Conversely, standard-precision Transformers efficiently handle these tasks with smaller models. Empirical experiments support the findings, highlighting the importance of numerical precision for improving LLMs’ mathematical reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are incredibly good at many things. But did you know they struggle with simple math problems? In this paper, scientists tried to figure out why. They found that when these models don’t use precise numbers, they can’t do basic math like adding or multiplying. But if they do use precise numbers, they can solve these problems easily. This is important because it helps us understand how we can make these models better at doing math. |
Keywords
* Artificial intelligence * Precision
