Summary of Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks, by Andrew Gambardella et al.
Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks
by Andrew Gambardella, Yusuke Iwasawa, Yutaka Matsuo
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper explores the arithmetic capabilities of large language models (LLMs), demonstrating their ability to accurately predict the first digit in multiplication tasks without using complex reasoning, despite requiring compounding operations. However, LLMs often struggle with predicting the last digit, even when it’s a simple 1-digit by 1-digit multiplication. The authors show that conditioning the model on higher-order digits improves its performance, increasing confidence by over 230% for Llama 2-13B and 150% for Mistral-7B in 5-digit by 5-digit multiplication tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can do lots of things. This paper talks about how well they can do math problems, especially multiplying numbers together. The researchers found that these models can usually get the first part of the answer right without thinking too much, but struggle to get the last part correct. They discovered that if they give the model more information about the bigger parts of the multiplication problem, it gets better at getting the final answer right. |
Keywords
» Artificial intelligence » Llama
