wandb/RAGTruth-processed
RAGTruth Dataset Dataset Description Dataset Summary The RAGTruth dataset is designed for evaluating hallucinations in text generation models, particularly in retrieval-augmented generation (RAG) contexts. It contains examples of model outputs along with expert annotations indicating whether the outputs contain hallucinations. Dataset Structure Each example contains: A query/question Context passages Model output Hallucination labels… See the full description on the dataset page: https://huggingface.co/datasets/wandb/RAGTruth-processed.
| Author / organization | wandb |
|---|---|
| Downloads | 2.9K (as reported by Hugging Face) |
| Likes | 31 |
| Created | 28 November 2024 |
| Last updated at source | 28 November 2024 |
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Connected in the knowledge graph
- Horizon-Labs/hallucination-guard-baseModel · used to train
- lebe1/lettuceprevent-ettin-decoder-68m-enModel · used to train
- ashiksharon005/verdict-layaModel · used to train
- vllm-sr/Decision-1.0-Route-0.6BModel · used to train
Recent changes
- 17 Oct 2026 Description: RAGTruth Dataset Dataset Description Dataset Summary The RAGTruth dataset is designed for evaluating hallucinations in text generation models, particularly in retrieval-augmented generation (RAG) contexts. It contains examples of model outp → RAGTruth Dataset Dataset Description Dataset Summary The RAGTruth dataset is designed for evaluating hallucinations in text generation models, particularly in retrieval-augmented generation (RAG) contexts. It contains examples of model outp
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Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.