The authors define a taxonomy for LLM-enhanced relational operators categorized into Select, Match, Impute, Cluster and Order, and release LROBench to evaluate single and multi-operator queries on semantic database processing.
CoRRabs/2405.04674(2024)
11 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
Larch uses a GNN-MDP formulation and a selectivity predictor plus dynamic programming to reorder semantic filter evaluation, cutting token usage 3x-19x versus prior systems on real and synthetic workloads.
Semantic Histograms treat semantic image filters as implicit range queries in embedding space and use two specificity estimators whose ensemble reduces end-to-end query optimization and execution overhead by up to 86%.
SEMA-SQL automates natural language to efficient hybrid queries combining relational algebra with LLM semantic operations via a new Hybrid Relational Algebra abstraction.
HoldUp uses LLM-guided clustering to provide holistic dataset context for semantic operators, yielding up to 33% higher classification accuracy and 30% higher scoring accuracy than row-by-row LLM processing across 15 datasets.
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.
Snowflake's Cortex AISQL adds native semantic operations to SQL via AI-aware optimization, adaptive model cascades, and semantic join rewriting, delivering 2-70x speedups in production workloads.
A unified framework and large-scale comparison of graph-based RAG methods on QA tasks yields new high-performing variants obtained by recombining existing components.
LLMs show performance degradation in multi-instance processing driven more strongly by instance count than by context length.
AnnoRetrieve induces annotation schemas from documents and answers queries via structured retrieval, reporting F1 0.87 with lower LLM cost than graph/LLM baselines but higher cost than QUEST and vector search.
citing papers explorer
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Large Language Model-Enhanced Relational Operators: Taxonomy, Benchmark, and Analysis
The authors define a taxonomy for LLM-enhanced relational operators categorized into Select, Match, Impute, Cluster and Order, and release LROBench to evaluate single and multi-operator queries on semantic database processing.
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Larch: Learned Query Optimization for Semantic Predicates
Larch uses a GNN-MDP formulation and a selectivity predictor plus dynamic programming to reorder semantic filter evaluation, cutting token usage 3x-19x versus prior systems on real and synthetic workloads.
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Selectivity Estimation for Semantic Filters on Image Data
Semantic Histograms treat semantic image filters as implicit range queries in embedding space and use two specificity estimators whose ensemble reduces end-to-end query optimization and execution overhead by up to 86%.
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SEMA-SQL: Beyond Traditional Relational Querying with Large Language Models
SEMA-SQL automates natural language to efficient hybrid queries combining relational algebra with LLM semantic operations via a new Hybrid Relational Algebra abstraction.
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Semantic Data Processing with Holistic Data Understanding
HoldUp uses LLM-guided clustering to provide holistic dataset context for semantic operators, yielding up to 33% higher classification accuracy and 30% higher scoring accuracy than row-by-row LLM processing across 15 datasets.
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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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Cortex AISQL: A Production SQL Engine for Unstructured Data
Snowflake's Cortex AISQL adds native semantic operations to SQL via AI-aware optimization, adaptive model cascades, and semantic join rewriting, delivering 2-70x speedups in production workloads.
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In-depth Analysis of Graph-based RAG in a Unified Framework
A unified framework and large-scale comparison of graph-based RAG methods on QA tasks yields new high-performing variants obtained by recombining existing components.
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Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length
LLMs show performance degradation in multi-instance processing driven more strongly by instance count than by context length.
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AnnoRetrieve: Efficient Structured Retrieval for Unstructured Document Analysis
AnnoRetrieve induces annotation schemas from documents and answers queries via structured retrieval, reporting F1 0.87 with lower LLM cost than graph/LLM baselines but higher cost than QUEST and vector search.
- Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework