CoolFace
29 shown

datasets

Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

modalityAllTextImageAudioTabularVideo
taskAllText generationQuestion answeringImage classificationSpeech
sizeAllUnder 1K1K – 10K100K – 1M1M – 10M
formatAllParquetJSONCSVWebDataset
sortTrendingMost likedMost usedRecently updatedNewest
Clear all
01Cleanlab /FinQA-hallucination-detection FinQA Hallucination Detection Dataset Summary This dataset was created from a subset of the original FinQA dataset. For each user query (financial questions), we prompted an LLM to generate a response to this query based on provided context (financial statements and tables from the original FinQA). Each generated LLM response is labeled based on whether it is correct or not. This dataset is thus useful for benchmarking reference-free LLM Eval and Hallucination… See the full description on the dataset page: https://huggingface.co/datasets/Cleanlab/FinQA-hallucination-detection.text1K<n<10K2 likes1.4k downloads2y agoHugging Face02sachaarbonel /whisper-hallucinations Whisper Hallucinations on Noise Dataset Summary This dataset lists common hallucinations from OpenAI Whisper when the input has no speech. We build it from a noise-only corpus. We run Whisper on noise clips. We collect any non-empty text that Whisper outputs. We deduplicate phrases and count how often they occur. Use it to test, detect, and reduce non-speech hallucinations. Motivation ASR models often output text on silence or noise. These false hits harm UX… See the full description on the dataset page: https://huggingface.co/datasets/sachaarbonel/whisper-hallucinations.text1K<n<10K5 likes255 downloads1y agoHugging Face03seyled /Phantom_Hallucination_Detection Phantom: A Benchmark for Hallucination Detection in Financial Long-Context QA Authors: Lanlan Ji, Dominic Seyler, Gunkirat Kaur, Manjunath Hegde, Koustuv Dasgupta, Bing Xiang This is the repository containing the dataset for the submission mentioned above. This dataset is designed for hallucination detection in language models. It includes multiple variants of the Phantom dataset with different token lengths (seed, 2k, 5K, 10K, 20K, 30K) for long context experiments , segments… See the full description on the dataset page: https://huggingface.co/datasets/seyled/Phantom_Hallucination_Detection.text10K<n<100K2 likes241 downloads11mo agoHugging Face04reglab /legal_rag_hallucinations Dataset Card for Hallucination Free? Assessing the Reliability of Leading AI Legal Research Tools This data release contains the queries and raw model outputs we analyze in Magesh, Surani, Dahl, Suzgun, Manning and Ho, Hallucination Free? Assessing the Reliability of Leading AI Legal Research Tools, Journal of Empirical Legal Studies (2024, forthcoming). Consistent with emerging understanding of AI benchmarking and leaderboards, we reserve a random sample of 50% of the dataset to… See the full description on the dataset page: https://huggingface.co/datasets/reglab/legal_rag_hallucinations.textn<1K1 likes70 downloads2y agoHugging Face05kankshith123 /FinQA-hallucination-detection FinQA Hallucination Detection Dataset Summary This dataset was created from a subset of the original FinQA dataset. For each user query (financial questions), we prompted an LLM to generate a response to this query based on provided context (financial statements and tables from the original FinQA). Each generated LLM response is labeled based on whether it is correct or not. This dataset is thus useful for benchmarking reference-free LLM Eval and Hallucination… See the full description on the dataset page: https://huggingface.co/datasets/kankshith123/FinQA-hallucination-detection.text1K<n<10K0 likes61 downloads4mo agoHugging Face06tourist800 /LLM-Hallucination-Detection-complex-mathematics AIME Hallucination Detection Dataset This dataset is created for detecting hallucinations in Large Language Models (LLMs), particularly focusing on complex mathematical problems. It can be used for tasks like model evaluation, fine-tuning, and research. Dataset Details Name: AIME Hallucination Detection Dataset Format: CSV Size: (add size, e.g., 10MB) Files Included: AIME-hallucination-detection-dataset.csv: Contains the dataset. Content Description… See the full description on the dataset page: https://huggingface.co/datasets/tourist800/LLM-Hallucination-Detection-complex-mathematics.tabularn<1K1 likes59 downloads2y agoHugging Face07GD-ML /Taming-Hallucinations Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation CVPR 2026 Findings Project Page | Paper | Code Dataset Summary This repository hosts DualityVidQA, the large-scale paired video–QA dataset introduced in Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation. Taming Hallucinations introduces DualityForge, a controllable diffusion-based framework that turns real videos into… See the full description on the dataset page: https://huggingface.co/datasets/GD-ML/Taming-Hallucinations.textvideo-text-to-text1K<n<10K0 likes59 downloads4mo agoHugging Face08vkshdev /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.tabulartext-classification10K<n<100K0 likes59 downloads22d agoHugging Face09Anindita1979 /Phantom_Hallucination_Detection Phantom: A Benchmark for Hallucination Detection in Financial Long-Context QA Authors: Lanlan Ji, Dominic Seyler, Gunkirat Kaur, Manjunath Hegde, Koustuv Dasgupta, Bing Xiang This is the repository containing the dataset for the submission mentioned above. This dataset is designed for hallucination detection in language models. It includes multiple variants of the Phantom dataset with different token lengths (seed, 2k, 5K, 10K, 20K, 30K) for long context experiments , segments… See the full description on the dataset page: https://huggingface.co/datasets/Anindita1979/Phantom_Hallucination_Detection.text10K<n<100K1 likes53 downloads5mo agoHugging Face10tourist800 /AIME_Hallucination_Detection AIME Hallucination Detection Dataset This dataset is created for detecting hallucinations in Large Language Models (LLMs), particularly focusing on complex mathematical problems. It can be used for tasks like model evaluation, fine-tuning, and research. Dataset Details Name: AIME Hallucination Detection Dataset Format: CSV Size: (14.6 MB) Files Included: AIME-hallucination-detection-dataset.csv: Contains the dataset. Content Description The dataset… See the full description on the dataset page: https://huggingface.co/datasets/tourist800/AIME_Hallucination_Detection.tabularn<1K0 likes27 downloads2y agoHugging Face11wrom /Language-Vision-Hallucinations Dataset for Techen Project 095280 A comprehensive dataset for the Techen Project, focused on examining hallucinations in multi-modal AI-generated text by investigating model uncertainty, text generation patterns, and linguistic factors. Columns Overview image_link: URL to the image associated with each data row. temperature: Temperature setting for text generation, controlling output randomness. description: Text generated by the model for each image, using the… See the full description on the dataset page: https://huggingface.co/datasets/wrom/Language-Vision-Hallucinations.imagen<1K2 likes18 downloads2y agoHugging Face12Arthur-AI /python_wiki_hallucination_graded RAG + Instruction Following Results from Python Wikipedia benchmark This dataset is an artifact from an experiment conducted by Arthur Experiment We wanted to compare how good LLMs are at answering questions using a context. Doing this task well involves an inverse skill: recognizing when the necessary information to answer a question is absent, and choosing instead to not answer. One name for this is “staying grounded” in the context that you provide in your prompt to… See the full description on the dataset page: https://huggingface.co/datasets/Arthur-AI/python_wiki_hallucination_graded.textn<1K1 likes17 downloads2y agoHugging Face13benchmark-data /hallucination-traps Hallucination Traps A curated benchmark dataset consisting of intentionally misleading prompts designed to evaluate hallucination behavior in language models. Each prompt appears plausible at first glance but contains a subtle false premise, nonexistent entity, or incorrect factual assumption. The expected behavior is that the model should either refuse, express uncertainty, or explicitly identify the incorrect premise rather than hallucinate a confident but false answer.… See the full description on the dataset page: https://huggingface.co/datasets/benchmark-data/hallucination-traps.textn<1K0 likes15 downloads9mo agoHugging Face14SridharKumarKannam /neural-bridge-rag-hallucination RAG Hallucination Dataset Overview This dataset can be used to test hallucinations in Retrieval-Augmented Generation (RAG) systems.It is based on: neural-bridge/rag-hallucination-dataset-1000 Data Instances Each data point includes a context, a question about the context, and an answer. A typical example looks like this: { "context": "...", "question": "...", "answer": "..." } Data Fields context: Context to provide to the LLM. question: A question… See the full description on the dataset page: https://huggingface.co/datasets/SridharKumarKannam/neural-bridge-rag-hallucination.textn<1K0 likes14 downloads1y agoHugging Face15ClarusC64 /ai-5node-infer-buf-lag-cpl-hallucination-propagation-v0.1 What this repo does This dataset models hallucination propagation in agent ecosystems. It detects when inference pressure, weakened verification buffer, governance lag in review, and tight coupling through shared knowledge and automation cross the five-node cascade threshold into an unrecoverable hallucination propagation cascade. This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state.The fifth node represents the nonlinear… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-5node-infer-buf-lag-cpl-hallucination-propagation-v0.1.tabulartext-classificationn<1K0 likes14 downloads7mo agoHugging Face16Arthur-AI /hallucination_dolly_benchmarktextn<1K0 likes13 downloads1y agoHugging Face17Arthur-AI /hallucination_wikibiotextn<1K0 likes12 downloads1y agoHugging Face18cemuluoglakci /hallucination_evaluationtabular100K<n<1M1 likes11 downloads2y agoHugging Face19SGaleshchuk /XL_SUM_ukr_synthetic_hallucinationsDataset contains 1239 articles out of the validation set from https://huggingface.co/datasets/csebuetnlp/xlsum/blob/main/xlsum.py. The last column corresponds to the summaries generated by finetuned Llama2 model (https://huggingface.co/SGaleshchuk/Llama-2-13b-hf_uk_rank-32_ft). Generqted summaries have been also corrupted with GPT-4. Here is an algorithm applied: we extract named entities from the generated summaries using the Spacy NER model for Ukrainian and pass the first occurred entity… See the full description on the dataset page: https://huggingface.co/datasets/SGaleshchuk/XL_SUM_ukr_synthetic_hallucinations.textsummarization1K<n<10K1 likes11 downloads4mo agoHugging Face20C0uchP0tat0 /llm_hallucinationstabular1K<n<10K0 likes11 downloads2y agoHugging Face21JourneyBench /JourneyBench_Hallucinationimagequestion-answeringn<1K0 likes9 downloads2y agoHugging Face22grounded-ai /hallucination_testtextn<1K0 likes9 downloads2y agoHugging Face23LaRataAlada2024 /Hallucination_Evaluationtextn<1K0 likes9 downloads2y agoHugging Face24jiwon9703 /hallucination-sft-v0text10K<n<100K1 likes7 downloads10mo agoHugging Face25hb0810 /Generalized_Hallucination_Risk_Level_100k_Prompts Generalized Hallucination Risk Level — 100k Prompts A dataset of ~108,000 prompts labeled for hallucination risk (LOW / MEDIUM / HIGH) generated to train pre-inference classifiers for Large Language Models. Dataset Description Prompts were synthetically generated to cover a wide range of topics and difficulty levels, then labeled via a multi-model consensus protocol. Three independent LLMs evaluated each prompt; agreement determined the final risk label. Columns… See the full description on the dataset page: https://huggingface.co/datasets/hb0810/Generalized_Hallucination_Risk_Level_100k_Prompts.text100K<n<1M1 likes7 downloads4mo agoHugging Face26ClarusC64 /clinical-quad-prior-text-context-window-loss-new-data-summary-extension-hallucination-v0.1What this repo does This dataset models hallucinated narrative continuation in clinical summaries. It predicts when the interaction between prior text similarity, context window loss, lack of new data, and high summary extension rate indicates that new narrative content has been generated without supporting evidence. Core quad prior_text_similarity_index context_window_loss_index new_data_presence_index summary_extension_rate_index Prediction target label_hallucinated_continuation Row… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-prior-text-context-window-loss-new-data-summary-extension-hallucination-v0.1.tabulartext-classificationn<1K0 likes7 downloads7mo agoHugging Face27jason1966 /algozee_rag-based-hallucination-reduction-in-llms RAG-Based Hallucination Reduction in LLMs Introduction to Large Language Models and Hallucination Problem Dataset Info Source: Kaggle Original Size: 0.17 MB Kaggle Downloads: 43 Files: 1 Files llm_rag_dataset_6k.csv.csv Mirrored from Kaggle tabular1K<n<10K0 likes7 downloads6mo agoHugging Face282Phuong5Nam4 /Vietnamese_Hallucinationtextn<1K0 likes4 downloads1y agoHugging Face29Suman989 /smollm2-microbiology-hallucinations SmolLM2 Blind Spot Audit — Microbiology & Nepal Community Health Triage Overview This dataset contains 10 manually audited prompt-output pairs from HuggingFaceTB/SmolLM2-1.7B (base model, not instruct), testing its performance on two domain-specific categories: Microbiology laboratory protocols — Gram staining, serial dilution, PCR parameters, selective media interpretation Community health triage in Nepal — FCHV danger sign protocols, MUAC malnutrition thresholds… See the full description on the dataset page: https://huggingface.co/datasets/Suman989/smollm2-microbiology-hallucinations.textn<1K0 likes4 downloads6mo agoHugging Face

Listings come live from the Hugging Face Hub API. CoolFace does not host these files.