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| title: Honest | |
| emoji: 🤗 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 3.0.2 | |
| app_file: app.py | |
| pinned: false | |
| tags: | |
| - evaluate | |
| - measurement | |
| description: >- | |
| The HONEST score is a multilingual score that aims to compute how likely each language model is to produce hurtful completions based on a predefined set of prompts. | |
| # Measurement Card for HONEST | |
| ## Measurement description | |
| The HONEST score aims to measure hurtful sentence completions in language models. | |
| The score uses HurtLex, a multilingual lexicon of hurtful language, to evaluate the completions. | |
| It aims to quantify how often sentences are completed with a hurtful word, and if there is a difference between | |
| groups (e.g. genders, sexual orientations, etc.). | |
| ## How to use | |
| When loading the model, specify the language of the prompts and completions. | |
| The available languages are: 'it' (Italian), 'fr' (French), 'es' (Spanish), 'pt' (Portuguese), 'ro' (Romanian), 'en' (English). | |
| ```python | |
| >>> honest = evaluate.load('honest', 'en') | |
| ``` | |
| Arguments: | |
| **predictions** (list of list of `str`): a list of completions to [HONEST prompts](https://huggingface.co/datasets/MilaNLProc/honest) | |
| **groups** (list of `str`) (*optional*): a list of the identity groups each list of completions belongs to. | |
| ## Output values | |
| `honest_score`: the HONEST score, representing the average number of hurtful completions across all groups | |
| `honest_score_per_group`: the HONEST score of each group separately. | |
| ### Values from popular papers | |
| In the [original HONEST paper](https://aclanthology.org/2021.naacl-main.191.pdf), the following scores were calculated for models, with Top K referring to the number of model completions that were evaluated: | |
| | Model Name | Top K =1 | Top K =5 |Top K =20 | | |
| | ---------------- | -------- | -------- | -------- | | |
| | UmBERTo (OSCAR) | 5.24 | 8.19 | 7.14 | | |
| | UmBERTo (Wiki) | 5.48 | 7.19 | 5.14 | | |
| | GilBERTo | 7.14 | 11.57 | 8.68 | | |
| | ItalianBERT XXL | 9.05 | 10.67 | 9.12 | | |
| | FlauBERT | 4.76 | 3.29 | 2.43 | | |
| | CamemBERT (OSCAR)| 18.57 | 9.62 | 7.07 | | |
| | CamemBERT (Wiki) | 7.62 | 4.90 | 4.19 | | |
| | BETO | 4.29 | 5.95 | 6.88 | | |
| | BERTimbau | 4.05 | 6.00 | 5.04 | | |
| | RomanianBERT | 4.76 | 3.90 | 4.61 | | |
| | BERT-base | 1.19 | 2.67 | 3.55 | | |
| | BERT-large | 3.33 | 3.43 | 4.30 | | |
| | RoBERTa-base | 2.38 | 5.38 | 5.74 | | |
| | RoBERTa-large | 2.62 | 2.33 | 3.05 | | |
| | DistilBERT-base | 1.90 | 3.81 | 3.96 | | |
| | GPT-2 (IT) | 12.86 | 11.76 | 12.56 | | |
| | GPT-2 (FR) | 19.76 | 19.67 | 17.81 | | |
| | GPT-2 (PT) | 9.52 | 10.71 | 10.29 | | |
| | GPT-2 (EN) | 17.14 | 12.81 | 13.00 | | |
| ## Examples | |
| Example 1: Calculating HONEST without groups | |
| ```python | |
| >>> honest = evaluate.load('honest', 'en') | |
| >>> completions = [['CEO', 'businessman', 'politician'], ['good', 'excellent', 'smart'], ['secretary', 'prostitute', 'maid'], ['beautiful', 'smart', 'tall']] | |
| >>> result = honest.compute(predictions=completions) | |
| >>> round(result['honest_score'],3) | |
| 0.167 | |
| ``` | |
| Example 2: Calculating HONEST with 2 groups (e.g. male/female) | |
| ```python | |
| >>> honest = evaluate.load('honest', 'en') | |
| >>> completions = [['CEO', 'businessman', 'father'], ['good', 'excellent', 'smart'], ['secretary', 'prostitute', 'maid'], ['beautiful', 'smart', 'tall']] | |
| >>> groups = ["male", "male", "female", "female"] | |
| >>> result = honest.compute(predictions=completions, groups = groups) | |
| >>> {g:round(s,3) for g,s in result['honest_score_per_group'].items()} | |
| {'male': 0.0, 'female': 0.167} | |
| ``` | |
| Example 2: Calculating HONEST in another language (French) | |
| ```python | |
| >>> honest = evaluate.load('honest', 'fr') | |
| >>> completions = [['PDG', 'avocat', 'père'], ['bon','excellent', 'intelligent'], ['secrétaire', 'prostituée', 'bonne'], ['belle', 'intelligente', 'grande']] | |
| >>> result = honest.compute(predictions=completions) | |
| >>> round(result['honest_score'],3) | |
| 0.083 | |
| ``` | |
| ## Citation | |
| ```bibtex | |
| @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", | |
| } | |
| ``` | |
| ```bibtex | |
| @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} | |
| } | |
| ``` | |
| ## Further References | |
| - Bassignana, Elisa, Valerio Basile, and Viviana Patti. ["Hurtlex: A multilingual lexicon of words to hurt."](http://ceur-ws.org/Vol-2253/paper49.pdf) 5th Italian Conference on Computational Linguistics, CLiC-it 2018. Vol. 2253. CEUR-WS, 2018. | |