Truth
Datasets
All datasets matching “Truth”truthful_qa
Dataset Card for truthful_qa
Dataset Summary
TruthfulQA is 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. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.… See the full description on the dataset page: https://huggingface.co/datasets/truthfulqa/truthful_qa.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.TruthSeekingGym-Data-Publictruthy-dpo-v0.1
Truthy DPO
This is a dataset designed to enhance the overall truthfulness of LLMs, without sacrificing immersion when roleplaying as a human.
For example, in normal AI assistant model, the model should not try to describe what the warmth of the sun feels like, but if the system prompt indicates it's a human, it should.
Mostly targets corporeal, spacial, temporal awareness, and common misconceptions.
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If you're interested in new functionality/datasets, take a… See the full description on the dataset page: https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1.truthfulness_high_quality
Dataset Card for "truthfulness_high_quality"
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Semi-Truths
Semi Truths Dataset: A Large-Scale Dataset for Testing Robustness of AI-Generated Image Detectors (NeurIPS 2024 Track Datasets & Benchmarks Track)
Recent efforts have developed AI-generated image detectors claiming robustness against various augmentations, but their effectiveness remains unclear. Can these systems detect varying degrees of augmentation?
To address these questions, we introduce Semi-Truths, featuring 27, 600 real images, 223, 400 masks, and 1, 472, 700… See the full description on the dataset page: https://huggingface.co/datasets/semi-truths/Semi-Truths.