Paper List for Instruction Tuning Data Selection

1. [NIPS2022]Generating training data with language models: Towards zero-shot language understanding
2. [EMNLP2022]Progen: Progressive zero-shot dataset generation via in-context feedback
3. [NIPS2023]When less is more: Investigating data pruning for pretraining llms at scale
4. [ICML2024]Curated llm: Synergy of llms and data curation for tabular augmentation in ultra low-data regimes
5. [NAACL2024]from quantity to quality: Boosting llm performance with self-guided data selection for instruction tuning
6. [ACL2024]Superfiltering: Weak-to-strong data filtering for fast instruction-tuning
7. [COLM2024]Instruction Mining: When Data Mining Meets Large Language Model Finetuning
8. [ICLR2024]Alpagasus:Training a better alpaca with fewer data
9. [ICLR2024]# instag: Instruction tagging for analyzing supervised fine-tuning of large language models
10. [ICLR2024]What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning
11. [ICML2024]Less: Selecting influential data for targeted instruction tuning
12. [ICLR2024]Self-alignment with instruction backtranslation
13. [ACL2024]One shot learning as instruction data prospector for large language models.
14. [EMNLP2024]Clustering and ranking:Diversity-preserved instruction selection through expert-aligned quality estimation
15. [ACL2024]IterSelectTune: An Iterative Training Framework for Efficient Instruction-Tuning Data Selection