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
Large Language Models (LLMs) enable us to better understand text documents, including PDFs and Word documents. However, LLMs, as well as more modern LLM agents, i.e., those with tool-calling abilities, typically treat such documents as plain text, ignoring the fact that they are often organized hierarchically into sect...
Ruiying Ma, Yi-Ming Lin, Aditya G. Parameswaran· 0 citations
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
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