Biases in MLM

This repository contains the code for the project Evaluation of Intrinsic and Extrinsic Bias for Debiased Language Model. Before running and exploring the code of the experiments, please read the instructions below.

Requirements

First, you should install the packages listed in requirements.txt.

pip install -r requirements.txt

Datasets

Due to size restrictions, some of the files are not in this repository, such as the embeddings glove.840B.300d.zip from Glove project. For a comprehensive list of files, plese review this Google Drive folder.

Running experiments

The experiments can be run through two files:

Computing intrinsic and extrinsic bias

Youy should use the file main.py to compute intrinsic and extrinsic measures. The file contains a comprehensive explanation of the arugments.

For example, if you want to compute CPS, SSS, and AULA intrinsic bias in StereSet and CrawlsPair datasets for RoBERTa model, you can run the below command in your terminal:

python main.py --lm_models roberta-large --datasets ss,cp --scores cps,sss,aula --bias_type intrinsic

Each metric and dataset in the arguments must be separated by comma only. If more models are wanted to be used in the experiments, they must be also listed and separated by comma.

Debiasing MLM using Auto-Debias approach

The file auto_debias.py should be uses for debiasing Masked Language Models (MLM) using Auto-Debias approach. For example, to debias bert-base-uncased under gender bias type, you should run the following in the terminal:

python auto_debias.py --debias_type gender --model_name_or_path bert-base-uncased --prompts_file prompts_bert-base-uncased_gender.txt

Note that a name of the prompts_file must be given, and it is assumed to be located in data/debiasing/. In the example, it is used prompts_bert-base-uncased_gender.txt, which contains the cloze-style prompts for bert-base-uncased based on gender biases.

In case of generate cloze-style prompts for another MLM and type of bias, you should use generate_prompts.py. For example, for albert-base-v2 model and race as bias type, you should use the following in your terminal

python generate_prompts.py --model_name_or_path albert-base-v2 --debias_typ race

In this study we use the cloze-style prompts for gender and race. For the former, we used bert-base-uncased, whereas in the alter we used albert-base-v2.