A labeled benchmark dataset for hallucination detection research in large language models.
20,000 factual prompts tested on GPT-2 (124M parameters), annotated with hallucination
type, internal activation signals, and prediction outcomes.
Dataset Details
Dataset Description
HallBench is released alongside the paper "Hallucination Fingerprints: Consistent Failure
Patterns in Large Language Models" (Upadhyay, 2026). It contains 20,000 factual… See the full description on the dataset page: https://huggingface.co/datasets/Trazemag/hallbench.
