BAT: Target-Instance-Free Data Preparation Synthesis via LLM-Driven Tree Search
Summary: BAT synthesizes executable data-preparation pipelines without target-table instances, supervision, or target access. An LLM-driven Monte Carlo tree search combines an action sandbox with execution-aware optimization, improving end-to-end pipeline accuracy by ≥8.74% over five baselines. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Congcong Ge (Zhejiang University)
- 2. Yachuan Liu (Zhejiang University)
- 3. Yixuan Tang (National University of Singapore)
- 4. Yifan Zhu (Zhejiang University)
- 5. Yaofeng Tu (ZTE Corporation)
- 6. Yunjun Gao (Zhejiang University)
BibTeX Citation
@inproceedings{ge_sigmod26,
title = {{BAT: Target-Instance-Free Data Preparation Synthesis via LLM-Driven Tree Search}},
author = {Ge, Congcong and Liu, Yachuan and Tang, Yixuan and Zhu, Yifan and Tu, Yaofeng and Gao, Yunjun},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802020},
url = {https://dl.acm.org/doi/10.1145/3802020},
year = {2026}
}
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