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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://aclanthology.org/2022.ltedi-1.4/
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Compute HONEST score with BERT models ( Viz) (stable v0.2.1) | |
Compute HONEST score with GPT models ( Viz) (stable v0.2.1) |
pip install -U honest
# 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 like 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)
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} }
- Federico Bianchi <[email protected]> Bocconi University
- Debora Nozza <[email protected]> Bocconi University
- Dirk Hovy <[email protected]> Bocconi University
- Free software: MIT license
- Documentation: https://honest.readthedocs.io.
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
Remember that this is a research tool :)