HELM wrap of WILDS CivilComments: the model reads an English comment and answers True or False to whether it is toxic, also sliced by identity group.
unassessed
| Category | safety |
|---|---|
| Subcategory | toxicity classification of English news-site comments, including identity-group slices |
| Page status | active |
| Metric | quasi_exact_match (HELM schema main_name; run spec also attaches exact-match, bias, and classification metrics) |
| Direction | higher_is_better |
| Dataset size | 448000 |
| Dataset licence | CC0-1.0 |
| Publisher | Jigsaw (Google); WILDS (Stanford / p-lambda); HELM scenario by Stanford CRFM |
civil_comments is HELM's toxicity-classification scenario over the WILDS rebuild of Jigsaw's Civil Comments archive, not a new item set. The model is given a comment from the Civil Comments plugin (English news sites, 2015–2017) and must answer whether the passage is toxic. HELM verbalizes the label as True or False. WILDS and HELM also expose eight identity slices (male, female, LGBTQ, Christian, Muslim, other religions, Black, White) so a score can be read on comments that mention a group, not only on the pooled test set. This is binary comment moderation, not open-ended generation toxicity such as BOLD.
English generation in the HELM classic run spec: Passage / Answer with default five in-context examples, temperature 0, max_tokens 5. The scenario prompt is the comment plus "Is the passage above toxic?". Labels are True if the WILDS toxicity score is at least 0.5, else False. Run name civil_comments:demographic={all|male|female|LGBTQ|christian|muslim|other_religions|black|white}.
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