Product-Fact-Grounded Video Ad Copy RLVR Dataset

#fact-grounded text generation #reward verification #unsupported claim detection #video advertising copy generation #product marketing content creation #advertising claim compliance

5000
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-03

AI Analysis & Value Prop

This dataset contains synthetic product materials, permitted facts, copywriting instructions, and video ad copy intended for voiceovers or on-screen text. Structured fields capture required-fact retention, factual support for atomic claims, and verifiable reward components, enabling evaluation of key-information coverage and unsupported product claims while supporting controlled text generation and RLVR training.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
copy_format	string	Indicates whether the generated content is for a video voiceover or on-screen text.
total_reward	number	The overall RLVR reward score aggregated from the reward rules.
atomic_claims	array	Product claims extracted from the generated copy, with the factual support status of each claim.
generated_copy	string	Video voiceover or on-screen copy generated from the product materials, permitted facts, and instruction.
permitted_facts	array	Facts allowed in the copy, with an indication of whether each fact is required.
reward_breakdown	object	Verifiable reward components for RLVR training, with each score normalized to a range from 0 to 1.
product_materials	object	Synthetic product information used as source material for generating copy.
verification_passed	boolean	The verification outcome based on required-fact retention and unsupported-claim checks.
required_fact_checks	array	Checks whether each fact marked as required is expressed in the generated copy.
generation_instruction	string	The original instruction specifying the copy objective, constraints, style, or other generation requirements.
unsupported_claim_count	integer	The number of product claims in the generated copy that cannot be supported by the permitted facts.
required_fact_retention_rate	number	The proportion of required facts retained in the copy, ranging from 0 to 1.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: Each sample may include synthetic product materials, permitted facts, copywriting instructions, and video voiceover or on-screen copy, along with atomic claim checks, required-fact retention checks, and reward information.

Which training and evaluation tasks does it support?: It can support controlled advertising copy generation, product fact consistency checks, required-information retention evaluation, and reinforcement learning with verifiable rewards. Specific uses should follow the field definitions and the dataset license.

What are the dataset size and file format?: The available information does not specify the sample count or delivery format. The field definitions indicate structured records containing text, arrays, objects, and numeric values. Confirm the actual size, format, and sample records with the provider before acquisition or use.

How can I obtain the dataset, and what are the licensing terms?: Check the dataset listing or contact the provider for access options, pricing, and license terms. Synthetic data is not automatically cleared for every use, so confirm the permitted scope and any restrictions before training, evaluation, or commercial use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Product-Fact-Grounded Video Ad Copy RLVR Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/e09eb266256944371e16938a446a9589},
  urldate={2026-10-19},
  keywords={video ad copy dataset,product fact generation,RLVR training data,advertising text generation,controlled copywriting,factual verification data,required fact retention,synthetic text data},
  version={1.0}
}

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