specific method · filed under data curation

CSAM filtering

Filtering excludes child sexual abuse material from data preparation or training data.

Also called child sexual abuse material filtering.

sources: 3
model: 1
lab adopt it: 1
strongest: used

How sources treat it

One count per evidence span, weakest treatment to strongest.

used 3

Documented in

Gemma 4 31B

Evidence

3 spans quoted from the sources, strongest treatment first.

Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process

useddata curationin Gemma 4 pre-training datasetGoogle DeepMind

google/gemma-4-26B-A4B-it · Hugging Face·Google DeepMind·2026-07-02

Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content.

useddata curationin Gemma 4 pre-training datasetGoogle DeepMind

google/gemma-4-31B-it · Hugging Face·Google DeepMind·2026-07-02

Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content.

useddata curationin Gemma 4 pre-training datasetGoogle DeepMind

Gemma 4 model card | Google AI for Developers·Google AI for Developers·2026-07-16

Filed alongside

Other methods under data curation :: data filtering.

Sensitive data filtering CBRN pre-training data filtering Filtering batched auto-generated and templated content Heuristic filtering Keyword- and regex-based filtering Model-based refusal and inability filtering Pass@100 filtering Pathological repetition filtering SmolVLM-based image-text quality scoring Unified data filtering pipeline Automated verification Coding-agent session filtering and trajectory deduplication Conservative model-based noise filtering Content quality and safety filtering Continuous contribution-score ranking Data cleaning Data filtering pipeline Dense annotation of ambiguous low-quality data Domain filtering with heuristics, classifier scoring, and deduplication Evidence-based data cleaning and training constraints Filtering low-information model-generated content Heuristic and statistical filtering with deduplication and quality models Image-text relevance filtering Long-context data cleaning pipeline