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Found 4 papers, 3 papers with code
A Survey on Data Selection for LLM Instruction Tuning
Instruction tuning is a vital step of training large language models (LLM), so how to enhance the effect of instruction tuning has received increased attention.
MoDS: Model-oriented Data Selection for Instruction Tuning
First, our approach utilizes a quality evaluation model to filter out the high-quality subset from the original instruction dataset, and then designs an algorithm to further select from the high-quality subset a seed instruction dataset with good coverage.
ChineseWebText: Large-scale High-quality Chinese Web Text Extracted with Effective Evaluation Model
Using our proposed approach, we release the largest and latest large-scale high-quality Chinese web text ChineseWebText, which consists of 1. 42 TB and each text is associated with a quality score, facilitating the LLM researchers to choose the data according to the desired quality thresholds.
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference
This paper proposes to perform natural language inference with Word-Pair-Dependency-Triplets.
