This is the official repo of the paper "Surface Form Competition: Why the Highest Probability Answer Isn't Always Right" We provide scripts for downloading/processing datasets and for reproducing our results on GPT-2 and GPT-3. We do not guarantee exact reproducibility, as library versions and GPUs may cause small differences, but these should be extremely minor.
We use python3 and pytorch 1.7.0, but we do not use cutting-edge features from either and expect to be largely forward and backward compatible. That is not a guarantee or promise.
You can use pip install -r requirements.txt to install the required libraries.
To use GPT-3 you must use OpenAI Beta, which is limited access. You can apply for access here. Once you have access you will need to point the score.py to your API key with the --key argument or put your key in api.key which is the default path.
DATA_README.md has thorough instructions for downloading and processing datasets. We provide automatic downloaders and processers for datasets where possible in data_downloaders/ but see DATA_README for full instructions.
Once you have a dataset downloaded, running all the zero-shot scoring strategies at once is as simple as:
python score.py <dataset abbrevation> --model <model>where <dataset-abbreviation> is the abbreviation for a given dataset used for table rows in the paper. If there is any confusion, simply look in score.py to see how dataset selection works. <model> is the name of either a GPT-2 or GPT-3 model e.g. xl, davinci, etc. To speed things up you can use a larger --batch if you have enough GPU memory.