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Revisiting Task-Oriented Dataset Search in the Era of Large Language Models: Challenges, Benchmark, and Solution

Summary: KATS constructs a dynamically updated task–dataset knowledge graph from scientific literature using multi-agent extraction, entity resolution, and hybrid vector/graph retrieval. CS-TDS benchmarks task-oriented search; KATS outperforms RAG baselines in effectiveness and efficiency. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14574
Venue
VLDB
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,627 | 27.09%
DOI
10.14778/3796195.3796209

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BibTeX Citation

@article{wei_vldb26,
        title = {{Revisiting Task-Oriented Dataset Search in the Era of Large Language Models: Challenges, Benchmark, and Solution}},
        author = {Wei, Zixin and Guo, Yucan and Li, Jinyang and Han, Xiaolin and Jin, Xiaolong and Ma, Chenhao},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {5},
        pages = {973--986},
        doi = {10.14778/3796195.3796209},
        url = {https://doi.org/10.14778/3796195.3796209},
        year = {2026}
}

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