qualifire/grounding-benchmark

The Grounding Claims Dataset is a multi-domain dataset for evaluating whether a natural language claim is grounded (i.e., supported or entailed) by a document. The dataset is organized into four subsets, each requiring different types of reasoning:

• general (1500 examples): Broad, everyday reasoning
• logical (1000 examples): Logical consistency and inference
• time_and_dates (100 examples): Temporal reasoning
• prices_and_math (100 examples): Numerical and mathematical reasoning

Each entry consists of:

• doc: A short context or passage
• claim: A natural language statement to verify against the doc
• label: A binary label indicating whether the claim is grounded in the document (1 for grounded, 0 for ungrounded)
• dataset: The source subset name (e.g., "general")

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📌 Features

Feature	Type	Description
doc	string	The document or passage providing the context
claim	string	A statement to verify against the document
label	string	grounded or ungrounded
dataset	string	The domain/subset the instance belongs to

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📊 Usage

This dataset can be used to train and evaluate models on factual verification, natural language inference (NLI), and claim grounding tasks across multiple domains.

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🏷️ Labels

• grounded — The claim is grounded in the document.
• ungrounded — The claim is ungrounded or contradicted by the document.