Summary of Occamllm: Fast and Exact Language Model Arithmetic in a Single Step, by Owen Dugan et al.
OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step
by Owen Dugan, Donato Manuel Jimenez Beneto, Charlotte Loh, Zhuo Chen, Rumen Dangovski, Marin Soljačić
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A framework for Large Language Models (LLMs) to accurately perform complex arithmetic operations without compromising speed or security is proposed. The approach uses the hidden states of a LLM to control a symbolic architecture that performs arithmetic, enabling exact calculations in a single autoregressive step. This framework, OccamLlama, achieves 100% accuracy on single arithmetic operations and outperforms GPT-4 with and without code interpreter on mathematical problem-solving benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do math! Right now, they’re not very good at it because they need to generate code first. This makes them slow and insecure. Our new framework changes that by letting the LLM itself do the math. We call this OccamLlama. It’s super fast, secure, and easy to understand. We tested it on lots of math problems and it got them all right! It even beats bigger models like GPT-4. |
Keywords
» Artificial intelligence » Autoregressive » Gpt
