Logic against bias

Here we provide the evaluation code for stereotypical bias on different tasks, including

• StereoSet
• Profession bias test
• Emotion bias test

The scripts in this folder are

• eval_stereo.py: evaluation on StereoSet.
• eval_gender.py: evaluation on profession/emotion tests.

Data and pretrained models

Download the test.json file from the bias-bench Github repo with this link, and put the test.json file under data/StereoSet/.

All pretrained models are hosted on Huggingface model hub.

Fairness experiments

StereoSet

Run the following command to reproduce the experiments on StereoSet

python eval_stereo.py MODEL_TYPE EVAL_SPLIT CLS_HEAD MODEL_NAME_STR SCORE_TYPE

The parameters have the following valid values:

• MODEL_TYPE:
  • sc for sequence classification (entailment models);
  • nsp for next sentence prediction (pretrained models)
• EVAL_SPLIT:
  • intrasentence
  • intersentence
• CLS_HEAD: the logit of the entailment model for final scoring
  • 0 for the entailment score
  • 1 for the neutral score
  • 2 for the contradictory score
  • Note - this parameter is ignored with MODEL_TYPE = nsp or SCORE_TYPE = pred
• MODEL_NAME_STR: the name of selected models
  • bert, deberta, roberta for both sc and nsp
  • bert-simcse, bert-simcse-unsup, roberta-simcse, roberta-simcse-unsup for nsp only.
• SCORE_TYPE: Specify scoring strategy
  • score: continous scoring
  • pred: discrete scoring
  • Note - this parameter is only used when MODEL_TYPE = sc

For example, the following command runs the intrasentence task with the ESP-deberta-large entailment model.

python eval_stereo.py sc intrasentence 0 deberta score

Gender-profession-emotion test

Run the following command to reproduce the experiments on gender-profession-emotion test

python eval_gender.py MODEL_TYPE CLS_HEAD MODEL_NAME_STR SCORE_TYPE

The only difference is that we no longer specify the evaluation split here and the code will run both profession and emotion tests. For example,

python eval_gender.py nsp 0 roberta-simcse score
python eval_gender.py sc 0 roberta pred

General language understanding tasks (GLUE)

We construct suppositions for each test sample and predict True / Neutral / False truth values of the suppositions. Here we list some example suppositions. Our ESP models can be zero-shot text classifiers, including

• luohy/ESP-bert-large
• luohy/ESP-roberta-large
• luohy/ESP-deberta-large

For classification, we associate the task-specific labels with the truth values of the constructed suppositions as follows.

• QNLI

The answer to {question} is entailed by {context}.

• QQP

The answer to {question 1} is entailed by the answer to {question 2}.

• RTE

{hypothesis} is entailed by {premise}

• CoLA

The sentence is not fluent is entailed by the sentence is "{sentence}".

• SST2

The movie is good is entailed by {movie review}.

Note that the the relation between the final predictions and truth values might vary. For example, one can use the following supposition for SST2

The movie is bad is entailed by {movie review}.

If the supposition is True, the review would be negative.

Citation

If you used our code or model, please cite the paper. If there is any question, please feel free to contact the first author HL. Thank you!