HONEST: Measuring Hurtful Sentence Completion in Language Models
Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that 4.3{\%} of the time, language models complete a sentence with a hurtful word. These cases are not random, but follow language and gender-specific patterns. We propose a score to measure hurtful sentence completions in language models (HONEST). It uses a systematic template- and lexicon-based bias evaluation methodology for six languages. Our findings suggest that these models replicate and amplify deep-seated societal stereotypes about gender roles. Sentence completions refer to sexual promiscuity when the target is female in 9{\%} of the time, and in 4{\%} to homosexuality when the target is male. The results raise questions about the use of these models in production settings.
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Colab
Datasets
Introduced in the Paper:
HONEST
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Hurtful Sentence Completion | HONEST | BERT-base | HONEST | 1.19 | # 1 | |
| Hurtful Sentence Completion | HONEST | DistilBERT-base | HONEST | 1.90 | # 2 | |
| Hurtful Sentence Completion | HONEST | RoBERTa-large | HONEST | 2.62 | # 4 | |
| Hurtful Sentence Completion | HONEST | RoBERTa-base | HONEST | 2.38 | # 3 | |
| Hurtful Sentence Completion | HONEST | BERT-large | HONEST | 3.33 | # 5 |
