Summary of Pre-calc: Learning to Use the Calculator Improves Numeracy in Language Models, by Vishruth Veerendranath et al.
Pre-Calc: Learning to Use the Calculator Improves Numeracy in Language Models
by Vishruth Veerendranath, Vishwa Shah, Kshitish Ghate
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Pre-Calc, a simple pre-finetuning objective, improves numerical comprehension in language models by learning to use calculators for both encoder-only and encoder-decoder architectures. The proposal is formulated as discriminative (BERT, RoBERTa) or generative (Flan-T5) tasks on datasets like MAWPS, SVAMP, and AsDiv-A. Pre-training leads to better performance on downstream tasks that require numerical understanding. By leveraging calculators, smaller language models can improve mathematical reasoning, which is essential in education and finance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to solve math problems without a calculator! This paper helps machines get better at math by teaching them how to use calculators like humans do. They try it out on different machine learning models and datasets. The results show that this approach makes the machines better at understanding numbers and doing math, which is important for things like education and finance. |
Keywords
» Artificial intelligence » Bert » Encoder » Encoder decoder » Machine learning » T5
