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Project description
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 for six languages (English, Italian, French, Portuguese, Romanian, and Spanish).
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
Installing
pip install -U honest
Using
# Load BERT model
tokenizer = AutoTokenizer.from_pretrained(name_model)
model = AutoModelWithLMHead.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 entry 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", }
Development Team
Federico Bianchi <f.bianchi@unibocconi.it> Bocconi University
Debora Nozza <debora.nozza@unibocconi.it> Bocconi University
Dirk Hovy <dirk.hovy@unibocconi.it> 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.
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