Categoryexternal
MeasureDecision classification
TasksDetecting unsupported claims in retrieved-context answers
DifficultyDepends on the evaluated split and label mapping
RAGTruth annotates unsupported or contradictory content in answers generated with retrieved evidence. It supports evaluation of hallucination detectors across question answering, summarization, and data-to-text tasks.
No model in the catalog has a published RAGTruth score.The benchmark is defined by RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models, but the catalog carries no value for it yet. An absent value is shown as absent here rather than as a zero.
About RAGTruth
What the benchmark measures and how its value is published, from the source’s own record.
What is RAGTruth?
RAGTruth is an external benchmark published by RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models. RAGTruth annotates unsupported or contradictory content in answers generated with retrieved evidence. It supports evaluation of hallucination detectors across question answering, summarization, and data-to-text tasks. Its evaluation set is described by the source as detecting unsupported claims in retrieved-context answers. The source rates its difficulty as depends on the evaluated split and label mapping.
How is RAGTruth scored?
Each model's result is published by RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models as decision classification. ModelScale prints that value exactly as published, and never converts or rescales it. This page ranks the 0 models that carry a published value, highest value first; a model the source never scored is left out rather than ranked at zero. The source does not state whether a higher value is the better result, so neither does this page.
ModelScale