honest 0.2.1

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HONEST: Measuring Hurtful Sentence Completion in Language Models

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Large language models (LLMs) have revolutionized the field of NLP. However, LLMs capture and proliferate hurtful stereotypes, especially in text generation. We propose HONEST, a score to measure hurtful sentence completions in language models. It uses a systematic template- and lexicon-based bias evaluation methodology in six languages (English, Italian, French, Portuguese, Romanian, and Spanish) for binary gender and in English for LGBTQAI+ individuals.

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See the papers for additional details:

Nozza D., Bianchi F., and Hovy D. “HONEST: Measuring hurtful sentence completion in language models.” The 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2021. https://aclanthology.org/2021.naacl-main.191

Nozza D., Bianchi F., Lauscher L., and Hovy D. “Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals.” The Second Workshop on Language Technology for Equality, Diversity and Inclusion at the Annual Meeting of the Association for Computational Linguistics 2022. https://milanlproc.github.io/publication/2022-honest-hurtful-language-model-lgbtqia+/

Tutorials

Name	Link
Compute HONEST score (+Viz) (stable v0.2.0)

Installing

pip install -U honest

Using

# Load HONEST templates
evaluator = honest.HonestEvaluator(lang)
masked_templates = evaluator.templates(data_set="binary") # or "queer_nonqueer" or "all"

# Load BERT model
tokenizer = AutoTokenizer.from_pretrained(name_model)
model = AutoModelForMaskedLM.from_pretrained(name_model)

# Define nlp_fill pipeline
nlp_fill = pipeline('fill-mask', model=model, tokenizer=tokenizer, top_k=k)

print("FILL EXAMPLE:",nlp_fill('all women likes to [M].'.replace('[M]',tokenizer.mask_token)))

# Fill templates (please check if the filled words contain any special character)
filled_templates = [[fill['token_str'].strip() for fill in nlp_fill(masked_sentence.replace('[M]',tokenizer.mask_token))] for masked_sentence in masked_templates.keys()]

honest_score = evaluator.honest(filled_templates)
print(name_model, k, honest_score)

Citation

Please use the following bibtex entries if you use this score in your project:

@inproceedings{nozza-etal-2021-honest,
title = {"{HONEST}: Measuring Hurtful Sentence Completion in Language Models"},
author = "Nozza, Debora and Bianchi, Federico  and Hovy, Dirk",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.191",
doi = "10.18653/v1/2021.naacl-main.191",
pages = "2398--2406",
}

@inproceedings{nozza-etal-2022-measuring,
    title = {Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals},
    author = "Nozza, Debora and Bianchi, Federico and Lauscher, Anne and Hovy, Dirk",
    booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
    publisher = "Association for Computational Linguistics",
    year={2022}
}

Development Team

• Federico Bianchi <[e-mail]> Bocconi University

• Debora Nozza <[e-mail]> Bocconi University

• Dirk Hovy <[e-mail]> Bocconi University

Software Details

• Free software: MIT license

• Documentation: https://honest.readthedocs.io.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Note

Remember that this is a research tool :)

History

0.1.0 (2022-01-25)

• First release on PyPI.