PLOP is a cost-based optimizer that finds optimal placements for semantic LLM operators in hybrid query plans via dynamic programming, delivering up to 1.5x speedup and 4.29x cost reduction on 44 benchmark queries while preserving accuracy.
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AnnoRetrieve uses auto-generated structured schemas and queries to retrieve information from unstructured documents more efficiently and accurately than embedding-based methods.
MoDora introduces local-alignment aggregation, a Component-Correlation Tree, and question-type-aware retrieval to improve accuracy on semi-structured document QA by 5.97-61.07% over baselines.
Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.
citing papers explorer
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AnnoRetrieve: Efficient Structured Retrieval for Unstructured Document Analysis
AnnoRetrieve uses auto-generated structured schemas and queries to retrieve information from unstructured documents more efficiently and accurately than embedding-based methods.
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MoDora: Tree-Based Semi-Structured Document Analysis System
MoDora introduces local-alignment aggregation, a Component-Correlation Tree, and question-type-aware retrieval to improve accuracy on semi-structured document QA by 5.97-61.07% over baselines.
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Access Paths for Efficient Ordering with Large Language Models
Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.