DebiasingConGater

This repository is dedicated to the models of the paper Controllable Gate Adapters (ConGater) Accepted to main conference of EACL 2024.

The infrastructure of the code is similar to the ModularizedDebiasing from the papers:

Modular and On-demand Bias Mitigation with Attribute-Removal Subnetworks

and

Parameter-efficient Modularised Bias Mitigation via AdapterFusion

Tasks and Environments

Classification Task

Dataset

• All the datasets for the classification task are publicly available for download.
• We have a pre-processed version of the data for train, eval and test in seperate .pkl file which we use to call the data loader and Dataset file
• Due to the size of the dataset we are not able to provide them in the repository but we provided the config file for the dataset (Without Path)

Environment

To Install the environment after instilling Conda or Mini conda run the command

conda env create -f cls_congater.yml

After installation is complete you can access the environment by running :

conda activate cls_congater

Structure of the code

• scripts: Folder contains utility codes for adversarial mode, checkpoint, generating embeddings and etc.

• src: Folder contains utility codes for attacking , evaluating , logging and data handler

• models: Subfolder of src contains all the models that can be called for training which contain , Baseline, Baseline adv, Adapter, Adapter Adv and ConGater

• cfg.yml: contains all the training and dataset configurations required to run the training with default values

• main_attack.py: can be used to attack an already trained model (normally called with main.py)

• main.py: contains train wrappers and arguments to overwrite default config file.

Changing Code using Arguments:

To run the code you can use the following python command.

python main.py --gpu_id=0 --ds=hatespeech --model_type=congater --training_method=par --model_name=mini --random_seed --num_runs=1 --gate_squeeze_ratio=12 --log_wandb

• --gpu_id: sets the id of the gput that you are using by default 0
• --ds: sets the dataset that you want to run the model on
• --model_type: sets the model type that you want to run (baseline, baseline_adv,adapter_baseline, adapter_adv, congater)
• --training_method: For ConGater you can select parallel (par) training or post-hoc(post) training
• --model_name: sets the model to run (bert,mini,roberta-base) are the values
• --random_seed: activates random seeding for several runs to insure we dont have seed selection bias
• --num_runs: select how many times you want the current cofig to run , (always use with --random_seed)
• --gate_squeeze_ratio: selects the bottleneck for the ConGater , for adapter the default values are in the cofig based on previous papers
• --log_wandb: Starts logging the runs on wandb
• --wandb_project: sets the prefix of the project, dataset name is added to the project name by default

For More control arguments please check out main.py file line 543-580.

Paper citation:

@inproceedings{masoudian2024congater,
	title        = {Effective Controllable Bias Mitigation for Classification and Retrieval using Gate Adapters},
	author       = {Shahed Masoudian and Cornelia Volaucnik and Markus Schedl and Navid Rekabsaz},
	year         = 2024,
	booktitle    = {Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics},
	publisher    = {Association for Computational Linguistics},
	address      = {Malta}
}

Information Retrieval Task