This project explores the capability of Large Language Models (LLMs), particularly BERT-based models, to understand and perform arithmetic tasks involving numerals. We fine-tuned models on single-digit addition and subtraction and extended our research to cover selective double-digit numerals.

The datasets were constructed using two templates of arithmetic equations involving the operators + (plus) and - (minus), ensuring non-negative results. The training dataset contains 3600 samples, with additional datasets for testing single-digit (1860 samples) and double-digit (7200 samples) numerals.

• Epochs: 10
• Batch size: 32
• Learning rate: Started at 2.0e-4, decreasing exponentially to 2.0e-5

This preprocessing strategy aims to improve numeral understanding by focusing on positional values, breaking down numerals into individual digits (e.g., "23" becomes "2 3").

A modified masking approach targets spans of consecutive digits, enhancing the model's ability to understand and predict entire numerals within arithmetic contexts.

To set up the project, follow these steps:

1. Clone the repository:

git clone http:https://github.com/abbassix/arithBERT

(optional) 1½. Create a virtual environment:

python3.10 -m venv .venv

1½.a. On Windows:

.venv\Scripts\activate

1½.b. On macOS/Linux:

source .venv/bin/activate

2. Install the required dependencies:

pip install -r requirements.txt

To run the project, you need to create the training and test datasets. But before that you need to have a directory named datasets in the parent directoy of the project to store the datasets there. You will also need to have two more directories: models to load to store models and results to store the resulting accuracies. To make sure if you have the required directories and if not, to create them, run the following lines.

chmod +x create_dirs.sh
./create_dirs.sh

Then you need to run the following lines to create 6 different training and test datasets.

chmod +x create_datasets.sh
./create_datasets.sh

After creating datasets, you have to fine-tune the models. If the models are already stored in the models directory they will be loaded from there, otherwise they will be downloaded from Hugging Face Hub. The arguments to fine-tune the modela are stored in three different YAML files. Run the following lines to fine-tune three different scenarios to compare the results.

chmod +x finetune.sh
./finetune.sh

Finally we have to compare the results with graphs. Run the following line to create the graphs in the results directory.

python graphs.py