mlsanityData to 7 Oct 2026

IterSelectTune: An Iterative Training Framework for Efficient Instruction-Tuning Data Selection

arXiv 2410.13464 ↗First posted 17 Oct 2024cs.CLcited by 6

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Abstract (arXiv)

As large language models (LLMs) continue to advance, instruction tuning has become critical for improving their ability to generate accurate and contextually appropriate responses. Although numerous instruction-tuning datasets have been developed to enhance LLM performance, selecting high-quality instruction data from large source datasets typically demands significant human effort. In this work, we introduce IterSelectTune, an efficient, cost-effective iterative training policy for selecting high-quality instruction data with no human involvement and limited reliance on GPT-4. By fine-tuning on approximately 20% of the source data, our method consistently outperforms models fine-tuned on the full dataset across multiple benchmarks and public test datasets. These results highlight the effectiveness of our approach in enhancing LLM performance while reducing the computational resources required for instruction tuning.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 35 of its 47 references that are in the map. First posted on arXiv 17 Oct 2024.

Builds on

35 of 47 references.

References · 35

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23 more
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  16. 2024Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuningcited by 134
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metriccited by 24
  2. 2025LEAD: Iterative Data Selection for Efficient LLM Instruction Tuningcited by 13
  3. 2025Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoningcited by 7
  4. 2026Self-Improvement of Large Language Models: A Technical Overview and Future Outlookcited by 5
  5. 2025MergeIT: From Selection to Merging for Efficient Instruction Tuningcited by 3
  6. 2026MADS: Model-Aware Diverse Core Set Selection for Instruction Tuningcited by 0