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LEAP: LLM-powered End-to-end Automatic Library for Processing Social Science Queries on Unstructured Data

Summary: LEAP end-to-end processes vague social-science queries over unstructured data by filtering nondeterministic requests, selecting ML annotation functions, and generating executable code. On QUIET-ML, it achieves 92% pass@1 at $1.06/query. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14086
Venue
VLDB
Year
2025
Pagerank
5.2634238e-05
Overall Rank
9,466 | 35.06%
DOI
10.14778/3705829.3705843

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{hu_vldb25,
        title = {{LEAP: LLM-powered End-to-end Automatic Library for Processing Social Science Queries on Unstructured Data}},
        author = {Hu, Chuxuan and Peters, Austin and Kang, Daniel},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {2},
        pages = {253--264},
        doi = {10.14778/3705829.3705843},
        url = {https://doi.org/10.14778/3705829.3705843},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,286 ScaleDoc: Scaling LLM-based Predicates over Large Document Collections 2026 SIGMOD 5.093636e-05
10,360 Drama: Unifying Data Retrieval and Analysis for Open-Domain Analytic Queries 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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