This dataset focuses on speech recognition tasks for academic lecture scenarios.

Train models to turn advertising briefs into structured social media creative that fits directly into advertising workflows.

Train precise citation coverage and completeness checks with synthetic social ads and structured evidence annotations.

Train models to generate clearly structured advertising text for outdoor media using verifiable layout constraints across multiple display formats.

Use synthetic recommendation copy and structured evidence labels to detect when personal experiences are improperly presented as universal facts.

Test whether video ad script reward validators can catch rule boundaries and evasion attempts with challenging compliant and near-miss pairs.

Train models to answer breaking-news questions with traceable evidence by following how reports confirm, revise, or qualify facts.

Help models detect outdated citations and evidence by testing whether social ad claims align with their sources over time.

The street dance action estimation video dataset is dedicated to improving the accuracy of dance action recognition and estimation.

Trace out-of-home ad claims from claim extraction through evidence linking to an overall fact-review conclusion.

Train models to detect conflicts across sources and judge whether citations adequately support synthetic social advertising claims.

Test whether numerical claims in social ads are genuinely supported by citations with explicit metric definitions and conditions.

Ground professional music licensing Q&A and key clause analysis in the contract text.

Examine comparative ad claims with structured evidence, qualifying conditions, and traceable fact-check labels.

Train models to spot outdated or inapplicable social media ad claims with structured temporal, geographic, availability, and evidence checks.