LCG applies centroid clustering and confidence-guided semi-supervised selection to curate 6K-sample subsets that yield superior MT-bench performance compared with prior filtering methods when used for instruction tuning.
Preprint, arXiv:2503.00034
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning
LCG applies centroid clustering and confidence-guided semi-supervised selection to curate 6K-sample subsets that yield superior MT-bench performance compared with prior filtering methods when used for instruction tuning.