The conceptual multiverse system with a verification framework for decision structures helps users in philosophy, AI alignment, and poetry build clearer working maps of open-ended problems by making implicit LLM choices explicit and changeable.
Parameswaran, and Eugene Wu
12 Pith papers cite this work. Polarity classification is still indexing.
12
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
roles
background 3polarities
background 3representative citing papers
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.
AnnoRetrieve uses auto-generated structured schemas and queries to retrieve information from unstructured documents more efficiently and accurately than embedding-based methods.
PrismaDV generates task-aware data unit tests by jointly analyzing downstream code and dataset profiles, outperforming task-agnostic baselines on new benchmarks spanning 60 tasks, with SIFTA enabling automatic prompt optimization that beats hand-written prompts.
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.
iPDB adds a predict operator and semantic query optimizations to SQL so that LLM and ML calls run efficiently inside the database, delivering 2.5x average and up to 30x speedup over prior systems.
ScaleDoc achieves over 2x end-to-end speedup and up to 85% fewer LLM invocations for semantic predicates on large document collections via offline LLM representations, contrastive-trained proxy filtering, and adaptive cascades.
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.
Blue DIL is a new architecture that unifies structured enterprise data, LLM world knowledge, and personal context through declarative query plans and agents for multi-source multi-modal applications.
An LLM-assisted workflow scales thematic analysis of millions of online posts and interviews, yielding themes that align and diverge from authoritative policy reports and serving as rough input for policy researchers.
Lightweight proxy models deliver over 100x cost and latency savings for semantic AI queries in databases with accuracy preserved or improved on benchmarks up to 10M rows.
SPEAR proposes structured prompt views, runtime adaptive refinement, and policy rules to make prompts first-class, versioned, and evolvable components in complex LLM applications.
citing papers explorer
-
Navigating the Conceptual Multiverse
The conceptual multiverse system with a verification framework for decision structures helps users in philosophy, AI alignment, and poetry build clearer working maps of open-ended problems by making implicit LLM choices explicit and changeable.
-
PLOP: Cost-Based Placement of Semantic Operators in Hybrid Query Plans
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.
-
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.
-
PrismaDV: Automated Task-Aware Data Unit Test Generation
PrismaDV generates task-aware data unit tests by jointly analyzing downstream code and dataset profiles, outperforming task-agnostic baselines on new benchmarks spanning 60 tasks, with SIFTA enabling automatic prompt optimization that beats hand-written prompts.
-
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.
-
iPDB -- Optimizing Semantic SQL Queries
iPDB adds a predict operator and semantic query optimizations to SQL so that LLM and ML calls run efficiently inside the database, delivering 2.5x average and up to 30x speedup over prior systems.
-
ScaleDoc: Scaling LLM-based Predicates over Large Document Collections
ScaleDoc achieves over 2x end-to-end speedup and up to 85% fewer LLM invocations for semantic predicates on large document collections via offline LLM representations, contrastive-trained proxy filtering, and adaptive cascades.
-
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.
-
Blue Data Intelligence Layer: Streaming Data and Agents for Multi-source Multi-modal Data-Centric Applications
Blue DIL is a new architecture that unifies structured enterprise data, LLM world knowledge, and personal context through declarative query plans and agents for multi-source multi-modal applications.
-
How can LLMs Support Policy Researchers? Evaluating an LLM-Assisted Workflow for Large-Scale Unstructured Data
An LLM-assisted workflow scales thematic analysis of millions of online posts and interviews, yielding themes that align and diverge from authoritative policy reports and serving as rough input for policy researchers.
-
100x Cost & Latency Reduction: Performance Analysis of AI Query Approximation using Lightweight Proxy Models
Lightweight proxy models deliver over 100x cost and latency savings for semantic AI queries in databases with accuracy preserved or improved on benchmarks up to 10M rows.
-
Making Prompts First-Class Citizens for Adaptive LLM Pipelines
SPEAR proposes structured prompt views, runtime adaptive refinement, and policy rules to make prompts first-class, versioned, and evolvable components in complex LLM applications.