datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
DeepSeek-R1-Distill-Qwen-1.5B-Self-CalibrationThis dataset contains data for the paper Efficient Test-Time Scaling via Self-Calibration.
We propose an efficient test-time scaling method by using model confidence for dynamically sampling adjustment, since confidence can be seen as an intrinsic measure that directly reflects model uncertainty on different tasks. For example, we can incorporate the model’s confidence into self-consistency by assigning each sampled response $y_i$ a confidence score $c_i$. Instead of treating all responses… See the full description on the dataset page: https://huggingface.co/datasets/HINT-lab/DeepSeek-R1-Distill-Qwen-1.5B-Self-Calibration.Llama_3.1-8B-Instruct-Self-CalibrationThe official repository which contains the code and pre-trained models/datasets for our paper Efficient Test-Time Scaling via Self-Calibration.
🔥 Updates
[2025-3-3]: We released our paper.
[2025-2-25]: We released our codes, models and datasets.
🏴 Overview
We propose an efficient test-time scaling method by using model confidence for dynamically sampling adjustment, since confidence can be seen as an intrinsic measure that directly reflects model… See the full description on the dataset page: https://huggingface.co/datasets/HINT-lab/Llama_3.1-8B-Instruct-Self-Calibration.mmlu_hinted_questions
MMLU Hinted Questions
Dataset Description
This dataset contains multiple-choice questions derived from MMLU and augmented with misleading hints. The misleading hints are intentionally designed to point to an incorrect answer.
The dataset was developed as part of the UnfaithRL project, which studies cue-following and unfaithful reasoning under reinforcement learning with verifiable rewards.
Specifically, it was used to investigate whether language models follow… See the full description on the dataset page: https://huggingface.co/datasets/UnfaithRL/mmlu_hinted_questions.