Awesome

Setup

Install all dependencies using conda:

conda env create -f environment.yml
conda activate lang-patching
pip install -e .

Training Pipeline

Note that this repository uses hydra for managing hyperparameters and experiments. The configs we use for training can be found under config. hydra creates unique outputs for every experiment under the directory output.

Creating Patch Finetuning Data

To start, create synthetic data for patch finetuning using the yaml format (some examples are in the PATCH_DIR folder), and then use convert_yaml_to_data.py to create json files. The JSON files used in our experiments can be found in the PATCH_DIR folder.

Training Patchable Models

The entry script for training patchable models is train_models.py. Run it as:

python train_models.py train.save_path={SAVE_PATH} +protocol={protocol} +patch_type={SUB_FOLDER} +multitask_sst=True +train.load_path={TASK_FINETUNED_MODEL} +learnt_interpreter={True/False}

• {SAVE_PATH}: path where the patchable model will be saved
• {protocol}: can be one of
  • simple: If you want to train a model on just the task ("Task Finetuning")
  • patch_finetuning_conds: train a patchable model for Sentiment Classification
  • patch_re: to train a patchable model for Relation Extraction
• {SUB_FOLDER}: one of the folders in the PATCH_DIR directory. To train models with override patches, use override_patch_data and to train a model with feature based patches, use feature_based_patch_data.
• learnt_interpreter: set this to True to train feature based patches.

Model Checkpoints

Checkpoints for models used in this work can be found at this link. We also provide notebooks to reproduce various Tables in the paper. To reproduce results:

• For Table-2, see the notebooks with checklist in the name
• For Table-3,4,5 please follow the instructions in the notebook override_patches_sentiment.ipynb and orig_model_results.ipynb
• For Figure-4, use finetuning_experiments.py

To cite this paper, use:

@inproceedings{murty2022patches,
    title = "Fixing Model Bugs with Natural Language Patches",
    author = "Murty, Shikhar  and
      Manning, Christopher  and
      Lundberg, Scott  and
      Ribeiro, Marco Tulio",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    year = "2022",
}