datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hallucination-autopsy-benchmark
Hallucination Autopsy Benchmark
A unified, standardized benchmark for cross-model, cross-parameter analysis of LLM hallucination phenomena.
Overview
This dataset merges multiple hallucination detection benchmarks into a single standardized schema, enabling systematic etiological analysis of why and how different LLM architectures hallucinate under specific configurations.
Version: 3.0.0Total Records: 69,002Base Records: 69,002Augmented Records: 0Hallucination… See the full description on the dataset page: https://huggingface.co/datasets/OiQ/hallucination-autopsy-benchmark.rag-hallucination-benchmark
RAG Hallucination Benchmark
Context
Retrieval-Augmented Generation (RAG) is the industry standard for reducing LLM hallucinations, but detecting when a RAG system fails is a massive challenge. Most existing benchmarks focus only on massive Deep Learning models and lack tabular features.
This dataset provides a clean, engineered setup to train models (from XGBoost to RoBERTa) to detect hallucinations, predict context faithfulness, and measure answer relevance.… See the full description on the dataset page: https://huggingface.co/datasets/vkshdev/rag-hallucination-benchmark.