CrowS-Pairs
Gemma 7B 51.3% leads since March 2024 Models choose between paired sentences that differ only in a stereotyped social group. Measures preference for the stereotyped sentence.
A score of 50 percent means no preference between stereotyped and anti-stereotyped sentences.
- Made by
- NYU (Nangia et al.)
- Released
- September 2020
- Kind
- Bias, fairness and politics
- Level
- Everyday
- Scored
- Checked automatically
- Answers
- Multiple choice
- Tasks
- All public
- Strongest model tested
- Gemma 2 27B #189 on the capability index
- Last in a system card
- July 2024
- Last model added
- July 2024
In the authors’ words
Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks. However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicitly creating harm with biased representations. To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs). CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age. In CrowS-Pairs a model is presented with two sentences: one that is more stereotyping and another that is less stereotyping. The data focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups. We find that all three of the widely-used MLMs we evaluate substantially favor sentences that express stereotypes in every category in CrowS-Pairs. As work on building less biased models advances, this dataset can be used as a benchmark to evaluate progress.
Gemma 7B leads with 51.3% among models released by June 2024
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
The newest model with a score here came out in June 2024, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
- Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1 Gemma 7B 51.3%
- 2 Gemma 2B 45.8%
- 3 Gemma 2 9B 37.5%
- 4 Gemma 2 27B 36.7%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Gemma 7B | 51.3% | Mar 2024 | Lab model card | |
| 2 | Gemma 2B | 45.8% | Mar 2024 | Lab model card | |
| 3 | Gemma 2 9B | 37.5% | Jul 2024 | Lab model card | |
| 4 | Gemma 2 27B | 36.7% | Jul 2024 | Lab model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
4 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Jul 31, 2024 | Gemma 2 9B technical report | 37.5 | CrowS-Pairs, top-1, IT (Table 18) | Bias |
| Jul 31, 2024 | Gemma 2 27B technical report | 36.7 | CrowS-Pairs, top-1, IT (Table 18) | Bias |
| Mar 13, 2024 | Gemma 7B technical report | 51.3 | CrowS-Pairs, top-1, IT (Table 8) | Bias |
| Mar 13, 2024 | Gemma 2B technical report | 45.8 | CrowS-Pairs, top-1, IT (Table 8) | Bias |
More in Safety and alignment
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Inkling 98.6% - Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. Gemini 4 Carbon 0%
- DistressBench In use Models reply as a chatbot to 718 simulated users in psychological crisis, in single-turn and multi-turn chats. Three judge models score replies against clinician-written rubrics.
Muse Spark 1.2 88.4% - Lakera B3 (Backbone Breaker Benchmark) In use Crowdsourced prompt-injection attacks hit backbone LLMs inside agent threat snapshots at three defense levels. The score is attack resistance, 100 minus the board's vulnerability percentage. Claude Opus 4.5 89%
- IHEval In use Models face conflicts between system, user and tool instructions. Reports a composite score for following the higher-priority instruction. GPT-4o 70%