Summary of Executing Arithmetic: Fine-tuning Large Language Models As Turing Machines, by Junyu Lai et al.
Executing Arithmetic: Fine-Tuning Large Language Models as Turing Machines
by Junyu Lai, Jiahe Xu, Yao Yang, Yunpeng Huang, Chun Cao, Jingwei Xu
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed Composable Arithmetic Execution Framework (CAEF) enables Large Language Models (LLMs) to learn step-by-step computations by emulating Turing Machines, gaining a genuine understanding of computational logic. This framework is highly scalable, allowing the composition of learned operators to significantly reduce the difficulty of learning complex operators. CAEF achieves nearly 100% accuracy across seven common mathematical operations on the LLaMA 3.1-8B model, supporting computations involving operands with up to 100 digits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are really smart at doing many language tasks. But they’re not so good at basic math like addition and subtraction. They just memorize examples instead of understanding how math works. This new framework helps them learn math by breaking it down into smaller steps, like a computer would do. It’s very good at adding big numbers together, even ones with 100 digits! |
Keywords
» Artificial intelligence » Llama
