Sentence to Model: Cost-Effective Data Collection LLM Agent
Summary: Four-stage, cost-aware data-collection pipeline—Explorer, Prioritizer, Extractor, Modeling—that outputs a dataset and a trained model from web, knowledge graphs, and internal networks. Budget-driven prioritization and LLM-based enrichment balance sources, rate limits, and latency, enabling rapid, end-to-end data-to-model delivery in a human–machine loop. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yael Einy (Tel Aviv University)
- 2. Guy Dar (Tel Aviv University)
- 3. Slava Novgorodov (Tel Aviv University)
- 4. Tova Milo (Tel Aviv University)
BibTeX Citation
@inproceedings{einy_sigmod25,
title = {{Sentence to Model: Cost-Effective Data Collection LLM Agent}},
author = {Einy, Yael and Dar, Guy and Novgorodov, Slava and Milo, Tova},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3725134},
url = {https://dl.acm.org/doi/10.1145/3722212.3725134},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 501 | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes | 2024 | VLDB | 0.00017267905 |
| 1,392 | Symphony: Towards Natural Language Query Answering over Multi-modal Data Lakes | 2023 | CIDR | 0.00010807936 |
| 11,454 | Datamap-Driven Tabular Coreset Selection for Classifier Training | 2025 | VLDB | 4.9793485e-05 |
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