Semantic Views: Certified Reuse of LLM Labels Across Semantic Queries
cs.DB, cs.AI
Submitted: 2026-10-08
Updated: 2026-10-08
Code: https://github.com/SemBench/SemBench
Sources
- Prediction-Powered Inference
- Semantic Caching for OLAP via LLM-Based Query Canonicalization (Extended Version)
- UQE: A Query Engine for Unstructured Databases
- From Interpretation to Compilation: A Compilation-Based Execution Engine for Semantic Operator Systems
- Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation
- MeanCache: User-Centric Semantic Caching for LLM Web Services
- CADENZA: Compiling Natural-Language Intent into Task-Specific Operator DAGs for Semantic Query Processing
- ConRAD: Conformal Risk-Aware Neural Databases
- Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm
- LLM-Driven Online Aggregation for Unstructured Text Analytics
- Fast LLM-Based Semantic Filtering: From a Unified Framework to an Adaptive Two-Phase Method
- iPDB -- Optimizing Semantic SQL Queries
- SemBench: A Benchmark for Semantic Query Processing Engines
- SEMA-SQL: Beyond Traditional Relational Querying with Large Language Models
- Streaming Model Cascades for Semantic SQL
- Compositional Online Learning for Semantic Data Processing Systems
- A Declarative System for Optimizing AI Workloads
- Optimizing LLM Queries in Relational Data Analytics Workloads
- Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptation
- PLOP: Cost-Based Placement of Semantic Operators in Hybrid Query Plans
Related papers
- Cost-Aware Mixture-of-Experts Coordination for Model Markets
- Vibe Coding on Trial: Operating Characteristics of Unanimous LLM Juries
- Human-Level Text-to-SQL via Reinforcement Learning on Verified Data, Without Pipeline Engineering
- MetaJoin: Adaptive Join Selections for Heterogeneous Tables
- Bridging Business Intent and Data: A Benchmark for Automatic Relational Data Product Generation
- DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation