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locuslab / TOFU

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TOFU: Task of Fictitious Unlearning šŸ¢ The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. Quick Links Website: The landing page for TOFU… See the full description on the dataset page: https://huggingface.co/datasets/locuslab/TOFU.

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README.md

TOFU

TOFU: Task of Fictitious Unlearning šŸ¢ The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. Quick Links Website: The landing page for TOFU… See the full description on the dataset page: https://huggingface.co/datasets/locuslab/TOFU.

Intended Uses

This model is registered on the QuantaMrkt quantum-safe registry. This model has not yet been PQC-verified.

Quick Start

# Install the CLI
pip install quantumshield

# Pull the model
quantumshield pull locuslab/TOFU

# Verify file integrity
quantumshield verify locuslab/TOFU

About

TOFU: Task of Fictitious Unlearning šŸ¢ The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set. Quick Links Website: The landing page for TOFU… See the full description on the dataset page: https://huggingface.co/datasets/locuslab/TOFU.

Created 2026-04-20
Downloads 134,191
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