license: odc-by
task_categories:
- text-generation
language: - en
Tulu-3-MIG-50K
Project | Github | Paper | HuggingFace's collection
MIG is an automatic data selection method for instruction tuning.
This dataset includes 50K high-quality and diverse SFT data sampled from Tulu3.
Performance
| Method | Data Size | ARC | BBH | GSM | HE | MMLU | IFEval | Avg_obj | AE | MT | Wild | Avg_sub | Avg |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pool | 939K | 69.15 | 63.88 | 83.40 | 63.41 | 65.77 | 67.10 | 68.79 | 8.94 | 6.86 | -24.66 | 38.40 | 53.59 |
| Random | 50K | 74.24 | 64.80 | 70.36 | 51.22 | 63.86 | 61.00 | 64.25 | 8.57 | 7.06 | -22.15 | 39.36 | 51.81 |
| ZIP | 50K | 77.63 | 63.00 | 52.54 | 35.98 | 65.00 | 61.00 | 59.19 | 6.71 | 6.64 | -32.10 | 35.69 | 47.44 |
| IFD | 50K | 75.93 | 63.56 | 61.03 | 49.39 | 64.39 | 53.60 | 61.32 | 12.30 | 7.03 | -20.20 | 40.83 | 51.08 |
| #InsTag | 50K | 72.54 | 64.80 | 69.83 | 48.17 | 63.50 | 65.99 | 64.14 | 6.58 | 6.84 | -20.70 | 38.21 | 51.17 |
| DEITA | 50K | 78.98 | 66.11 | 74.07 | 49.39 | 64.00 | 64.33 | 66.15 | 10.19 | 6.83 | -19.95 | 39.50 | 52.83 |
| CaR | 50K | 78.98 | 69.04 | 71.42 | 52.44 | 65.15 | 56.75 | 65.63 | 12.55 | 6.95 | -20.67 | 40.57 | 53.10 |
| QDIT | 50K | 79.66 | 65.42 | 70.74 | 53.05 | 65.06 | 57.30 | 65.21 | 15.78 | 6.76 | -20.56 | 41.03 | 53.12 |
| MIG | 50K | 80.00 | 66.39 | 72.02 | 57.93 | 64.44 | 65.06 | 67.64 | 14.66 | 7.32 | -17.77 | 42.99 | 55.32 |
Citation
@article{chen2025mig,
title={MIG: Automatic Data Selection for Instruction Tuning by Maximizing Information Gain in Semantic Space},
author={Chen, Yicheng and Li, Yining and Hu, Kai and Ma, Zerun and Ye, Haochen and Chen, Kai},
journal={arXiv preprint arXiv:2504.13835},
year={2025}
}