LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
Summary: LLM-agent framework for privacy-preserving, automatic data processing in fine-tuning: iteratively synthesizes/refines DP pipelines from prompts/feedback, avoiding raw-data inspection. Key accelerators: distribution-preserving sampling, low-quality target selection, cache-and-reuse; 10x faster search. (summarized by gpt-5.4-mini on Apr 12 2026)
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Authors
- 1. Wei Huang (Ant Financial)
- 2. Anda Cheng (Ant Financial)
- 3. Yinggui Wang (Ant Financial)
- 4. Lei Wang (Ant Financial)
- 5. Tao Wei (Ant Financial)
BibTeX Citation
@article{huang_vldb26,
title = {{LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning}},
author = {Huang, Wei and Cheng, Anda and Wang, Yinggui and Wang, Lei and Wei, Tao},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {5},
pages = {794--807},
doi = {10.14778/3796195.3796196},
url = {https://doi.org/10.14778/3796195.3796196},
year = {2026}
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Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,525 | DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data | 2023 | SIGMOD | 6.559044e-05 |
| 4,784 | Data-Juicer: A One-Stop Data Processing System for Large Language Models | 2024 | SIGMOD | 6.4132742e-05 |
| 6,159 | Automatic Data Acquisition for Deep Learning | 2021 | VLDB | 5.8653048e-05 |
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