ALC

Reference code for ACL22 paper - Answer-level Calibration for Free-form Multiple Choice Question Answering.

Dependencies

The code was written with, or depends on:

• Python 3.8
• Pytorch 1.9.1
• Transformers 4.11.3

Running the code

1. Create a virtualenv and install dependecies

   virtualenv -p python3.8 venv
   source env/bin/activate
   pip3 install -r requirements.txt

2. Set up the environment

   bash setup_env.sh

3. Run zero-shot experiment using

   bash run_zs.sh ${gpudev} ${dataname} ${split}

   Run k-shot experiment using

   bash run_fs.sh ${gpudev} ${dataname} ${split} {k}

   Valid datanames are COPA, commonsenseqa, mctaco, piqa, socialiqa, winogrande, arc_easy, arc_challenge, dream, swag and hendrycks_test. For hendrycks_test, additionally pass the category:

   bash run_zs.sh ${gpudev} ${dataname} ${split} "-data_config ${category}"

   where the category is in humanities, social_sciences, STEM, other

Citation

If you use this code, please consider citing:

[1] Sawan Kumar. 2022. Answer-level Calibration for Free-form Multiple Choice Question Answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 665–679, Dublin, Ireland. Association for Computational Linguistics. [bibtex]

Contact

For any clarification, comments, or suggestions please create an issue or contact [email protected]