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Table · dataset · 2022

TruthfulQA

Listed in Hugging Face Datasets

Dataset Card for TruthfulQA Dataset

Description

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.

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To perform well, models must avoid generating false answers… See the full description on the dataset page: huggingface.co/datasets/domenicrosati/TruthfulQA.

Links

Where it is published

Documentation and papers

Catalogue records · 1

Topics

Stated by source
question answering · text
Provenance · 1 source records, 10 field assertions
SourceKeyLast seenRaw
Hugging Face Datasetsdomenicrosati/TruthfulQA14 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:textsource · Hugging Faceconnector:huggingface@1.0.0
concepts[task].hf_task:question-answeringsource · Hugging Faceconnector:huggingface@1.0.0/tags[task_categories:*]
created_datesource · Hugging Faceconnector:huggingface@1.0.0
descriptionsource · Hugging Faceconnector:huggingface@1.0.0/description
licensesource · Hugging Faceconnector:huggingface@1.0.0/tags[license:*]
publication_datesource · Hugging Faceconnector:huggingface@1.0.0
titlesource · Hugging Faceconnector:huggingface@1.0.0/id
updated_datesource · Hugging Faceconnector:huggingface@1.0.0