datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rag_hallucinationsProvides examples of hallucinated responses for RAG applications.
llama2-hallucination-hidden-states
🧠 LLaMA-2 Hidden-State Hallucination Dataset
Repository: ShoaibSSM/llama2-hallucination-hidden-states
Task: Hallucination Detection via Transformer Internal Representations
Base Model: LLaMA-2-7B
Primary Labels: LLM-Judge + Hybrid Semantic Grounding
📌 Overview
This dataset contains layer-wise hidden states extracted from LLaMA-2-7B during question answering on SQuAD v2, along with structured hallucination labels.
Unlike traditional hallucination datasets that operate… See the full description on the dataset page: https://huggingface.co/datasets/ShoaibSSM/llama2-hallucination-hidden-states.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.audio-hallucination-attack
Audio Hallucination Attacks (AHA)
Dataset accompanying the paper "Audio Hallucination Attacks: Probing the Reliability of Large Audio Language Models"
It contains two subsets:
AHA-Eval (aha_eval.json) --- 6.5K QA pairs for benchmarking hallucination robustness in LALMs
AHA-Guard (aha_guard.json) --- 120K DPO preference pairs for post-alignment training
Audio Files
The audio files are provided as compressed archives in this repository:
File
Contents
Used by… See the full description on the dataset page: https://huggingface.co/datasets/aseth125/audio-hallucination-attack.rag-hallucination-dataset-1000
Retrieval-Augmented Generation (RAG) Hallucination Dataset 1000
Retrieval-Augmented Generation (RAG) Hallucination Dataset 1000 is an English dataset designed to reduce the hallucination in RAG-optimized models, built by Neural Bridge AI, and released under Apache license 2.0.
Dataset Description
Dataset Summary
Hallucination in large language models (LLMs) refers to the generation of incorrect, nonsensical, or unrelated text that does not stem from an… See the full description on the dataset page: https://huggingface.co/datasets/neural-bridge/rag-hallucination-dataset-1000.groundtruth-hallucination-bench-sample
Groundtruth Data Hallucination Benchmark Sample
This public teaser contains 180 representative, source-backed examples from Groundtruth Data products.
Groundtruth Data builds verified evaluation, remediation, and held-out validation datasets for AI models using authoritative source data. The commercial workflow is:
Find where a model fails.
Prove the failure with a larger verified evaluation.
Provide targeted remediation/training data.
Validate improvement on untouched held-out… See the full description on the dataset page: https://huggingface.co/datasets/Groundtruth-Data/groundtruth-hallucination-bench-sample.curatorkit-testrun-Hallucination
curatorkit-testrun-Hallucination
Built using CuratorKIT — provenance-grounded curation and synthesis for LLM post-training.
Method
qa
Backend
litellm
Model
openai/Qwen/Qwen2.5-0.5B-Instruct
Formats
alpaca
Artifact
dataset
Published
2026-08-28 10:47 UTC
Usage
from datasets import load_dataset
ds = load_dataset("ram-lexsi/curatorkit-testrun-Hallucination", "alpaca")
rag-hallucination-combined-dataset-v1
Dataset Description
This is a hybride dataset which is the result of fussion of several datasets. The dataset contains formated texts for chat models. This dataset is created particularly for TinyLlama-1.1b model. You will find the special tokens in each text. It can be used to fine tune any model that is pre-trained with the same special tokens as TinyLlama.
Special Features
One of the critical problem we face while inferencing with LLM that it often hallucinate. Which… See the full description on the dataset page: https://huggingface.co/datasets/muntasir2179/rag-hallucination-combined-dataset-v1.hallucination
Dataset Summary
EVE-Hallucination is a specialized dataset designed to evaluate language models' tendency to hallucinate (generate factually incorrect or unsupported information) in the Earth Observation (EO) domain. Unlike typical QA datasets that focus on correctness, this dataset contains deliberately hallucinated answers with detailed annotations marking which portions of the text are hallucinated.
This dataset is crucial for developing and evaluating hallucination detection… See the full description on the dataset page: https://huggingface.co/datasets/eve-esa/hallucination.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.Korean-Hallucination-Bench
Korean Hallucination Benchmark (한국어 환각 진단 벤치마크)
한국어 LLM의 환각(hallucination) 저항성을 평가하는 4지선다 벤치마크입니다. 법령·특허·행정·의료·금융 5개 전문 도메인에서, 한국어 특화 환각 유형 10종을 다룹니다. 각 문항은 사실 정답 1개와 전문가도 속을 만큼 그럴듯한 환각 오답 3개로 구성됩니다.
통계
총 10,167문항 (4지선다)
도메인(5): 법령 1,901 · 특허 2,190 · 행정 1,864 · 의료 2,113 · 금융 2,099
환각 유형(10): 수치·날짜 오류, 조항 왜곡, 근거 없는 추론, 한자어·신조어 혼재, 과잉 일반화, 멀티턴 맥락 붕괴, 존댓말·반말 역전, 출처 날조, 용어 왜곡, 사실 날조
구축 방법
문항 생성: Darwin-398B-JGOS (문제·선택지)
정답 검수·교정: Claude (Anthropic)… See the full description on the dataset page: https://huggingface.co/datasets/ginigen/Korean-Hallucination-Bench.med-hallucination-detection
Medical hallucination detection
A dataset for training a small model to detect hallucinations in medical answers
and explain why, by checking each answer against the context it should be
grounded in. Each row is a (question, answer, context) triple with a row_type:
not_hallucinated -- the answer is grounded in its context.
hallucinated -- the answer is not (sourced separately; see below).
The not_hallucinated split (this build)
Derived from MedQuAD, a collection… See the full description on the dataset page: https://huggingface.co/datasets/Certops/med-hallucination-detection.med-hallucination-detection-unfiltered
Medical hallucination detection (unfiltered)
A dataset for training a small model to detect hallucinations in medical answers
and explain why, by checking each answer against the context it should be
grounded in. Each row is a (question, answer, context) triple labelled row_type.
This is the unfiltered union of two sources: 7,464 grounded positives and
10,000 planted-hallucination negatives. It is the raw pool before sampling and
judging -- the downstream step samples from here… See the full description on the dataset page: https://huggingface.co/datasets/Certops/med-hallucination-detection-unfiltered.ko-hallucination-benchmark
Hallucination Benchmark v1
Dataset Description
Hallucination Benchmark는 한국어 LLM의 환각(Hallucination) 탐지 성능을 평가하기 위한 벤치마크 데이터셋입니다.
Dataset Summary
언어: 한국어 (Korean)
샘플 수: 244개
태스크: 6가지 (Coding, Dialogue, General, Math, QA, Summarization)
환각 타입: 5가지 (No Hallucination, Factual Contradiction, Factual Fabrication, Instruction Inconsistency, Logical Inconsistency)
Supported Tasks
이 벤치마크는 다음과 같은 태스크를 포함합니다:
Coding (40 samples): 코드 생성 및 프로그래밍 문제
Dialogue (40… See the full description on the dataset page: https://huggingface.co/datasets/jiwon9703/ko-hallucination-benchmark.phantasm-hallucination-benchmark
PHANTASM Hallucination Benchmark
Author: Vignesh S (vigneshwar234)
Paper: PHANTASM: Inverting LLM Failure Modes into Productive Features
Code: pip install phantasm-llm
GitHub: github.com/vignesh2027/PHANTASM
Overview
The PHANTASM Hallucination Benchmark is the companion dataset to the PHANTASM framework — the first ML system to mathematically invert LLM hallucination, confabulation, and epistemic miscalibration into productive features.
This dataset supports… See the full description on the dataset page: https://huggingface.co/datasets/vigneshwar234/phantasm-hallucination-benchmark.JourneyBench_Hallucination