Author

Aditya G. Parameswaran

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Preprint Aug 2026

Scout: Scalable Document Extraction via Data Similarity

Extracting values from large document collections powers data analysis across many domains. Frontier LLMs extract such values accurately, but processing an entire collection with one is prohibitively costly. Yet this cost is largely avoidable: real-world collections exhibit rich similarity, so for the same query over s...

Yi-Ming Lin, Chiyu Hao, Shreya Shankar et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Robust Hierarchical Structures for Agentic Document Analysis

This work proposes SHED, a two-stage workflow for inferring a robust and compact structure that improves F-1 scores by 13%--68% over non-LLM baselines and 9%--15% over expensive LLM-based approaches.

Ruiying Ma, Yi-Ming Lin, Aditya G. Parameswaran · 0 citations

#small language model Open access Jul 2026

Bolt-on, Verifiable Provenance for LLM-Powered Data Processing

BLIP is presented, a bolt-on framework for efficiently inferring a small-sized verifiable provenance for any LLM-powered data processing task, with any LLM, and eight strategies, each guaranteed to find a minimal verifiable provenance are introduced.

Yi-Ming Lin, Sepanta Zeighami, Aditya G. Parameswaran · Proceedings of the VLDB Endo... · 0 citations