datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TruthfulQA
Dataset Card for TruthfulQA
Dataset Summary
TruthfulQA: Measuring How Models Mimic Human Falsehoods
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers… See the full description on the dataset page: https://huggingface.co/datasets/domenicrosati/TruthfulQA.TruthfulQA_CoT_GPT4SciBench-TruthfulQA-RAGTruthfulQA_zhTruthfulQA dataset csv with question and answer field translated into Chinese by requesting GPT-4.
TruthfulQA_LLMstruthfulqa_indicOriginal Repository
Tasks (from original repository)
Generation (main task):
Task: Given a question, generate a 1-2 sentence answer.
Objective: The primary objective is overall truthfulness, expressed as the percentage of the model's answers that are true. Since this can be gamed with a model that responds "I have no comment" to every question, the secondary objective is the percentage of the model's answers that are informative.
Future Work:
Validate… See the full description on the dataset page: https://huggingface.co/datasets/vakyansh/truthfulqa_indic.truthfulqa_trTruthfulQA
Dataset Card for TruthfulQA
Dataset Summary
TruthfulQA: Measuring How Models Mimic Human Falsehoods
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers… See the full description on the dataset page: https://huggingface.co/datasets/M1STERPERFECT/TruthfulQA.TruthfulQA
Dataset Card for TruthfulQA
Dataset Summary
TruthfulQA: Measuring How Models Mimic Human Falsehoods
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers… See the full description on the dataset page: https://huggingface.co/datasets/jethalal23/TruthfulQA.truthful_qa_TrueFalse_Feedback
Dataset Card for Dataset Name
This is a reduced variation of the truthful_qa dataset (https://huggingface.co/datasets/truthful_qa), modified to associate boolean values with the given answers, with a correct answer as a reference, and a feedback.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information… See the full description on the dataset page: https://huggingface.co/datasets/nmarafo/truthful_qa_TrueFalse_Feedback.TruthfulQA
Dataset Card for TruthfulQA
Dataset Summary
TruthfulQA: Measuring How Models Mimic Human Falsehoods
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers… See the full description on the dataset page: https://huggingface.co/datasets/Kavya5705/TruthfulQA.TruthfulQA
Dataset Card for TruthfulQA
Dataset Summary
TruthfulQA: Measuring How Models Mimic Human Falsehoods
We propose a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. We crafted questions that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers… See the full description on the dataset page: https://huggingface.co/datasets/hamesh05/TruthfulQA.truthfulQA-boolTruthfulQA-Audited
TruthfulQA-Audited
Datasets accompanying an anonymous NeurIPS 2026 Evaluations & Datasets
Track submission on surface-form leakage in binary-choice truth
benchmarks. The release contains three related artifacts:
TruthfulQA-476
Cleaned subset of binary-choice TruthfulQA, with surface-form leakage
removed via an audit-and-prune procedure.
canonical_label: TruthfulQA-476
theta: 0.53
n_pairs: 476
audit AUC: 0.528
derived from: binary-choice TruthfulQA (790 pairs)… See the full description on the dataset page: https://huggingface.co/datasets/AnonymNeurIPS2026submission/TruthfulQA-Audited.truthful-qalikeTruthfulQA
TruthfulQA
tags: TruthFinder, QA, Dataset
Note: This is an AI-generated dataset so its content may be inaccurate or false
Dataset Description:
The 'TruthfulQA' dataset is designed to assist Machine Learning practitioners in training models for truthfulness detection in question-answering contexts. The dataset contains a collection of question-answer pairs, each with an associated label indicating whether the answer is deemed truthful or not, based on a curated source of verified… See the full description on the dataset page: https://huggingface.co/datasets/infinite-dataset-hub/TruthfulQA.truthfulqa_helm