REVIEW 15 cited by
Towards Accurate and Efficient Document Analytics with Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Towards Accurate and Efficient Document Analytics with Large Language Models
read the original abstract
Unstructured data formats account for over 80% of the data currently stored, and extracting value from such formats remains a considerable challenge. In particular, current approaches for managing unstructured documents do not support ad-hoc analytical queries on document collections. Moreover, Large Language Models (LLMs) directly applied to the documents themselves, or on portions of documents through a process of Retrieval-Augmented Generation (RAG), fail to provide high accuracy query results, and in the LLM-only case, additionally incur high costs. Since many unstructured documents in a collection often follow similar templates that impart a common semantic structure, we introduce ZenDB, a document analytics system that leverages this semantic structure, coupled with LLMs, to answer ad-hoc SQL queries on document collections. ZenDB efficiently extracts semantic hierarchical structures from such templatized documents, and introduces a novel query engine that leverages these structures for accurate and cost-effective query execution. Users can impose a schema on their documents, and query it, all via SQL. Extensive experiments on three real-world document collections demonstrate ZenDB's benefits, achieving up to 30% cost savings compared to LLM-based baselines, while maintaining or improving accuracy, and surpassing RAG-based baselines by up to 61% in precision and 80% in recall, at a marginally higher cost.
Forward citations
Cited by 15 Pith papers
-
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.
-
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 pr...
-
Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees
BARGAIN uses betting-based anytime-valid tests and adaptive, target-aware sampling to set model-cascade thresholds, delivering non-asymptotic quality guarantees and up to 86% greater cost savings than SUPG.
-
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.
-
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%.
-
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.
-
SEMA-SQL: Beyond Traditional Relational Querying with Large Language Models
SEMA-SQL formalizes Hybrid Relational Algebra to let users pose natural language questions answered by automatically generated queries that combine relational operators with LLM semantic reasoning, cutting LLM calls b...
-
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 ...
-
Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
A unified framework for LLM agent memory is benchmarked, with a new hybrid method outperforming state-of-the-art on standard tasks.
-
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.
-
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.
-
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.
-
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.
-
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.
-
A Survey on Open Dataset Search in the LLM Era: Retrospectives and Perspectives
A structured review of open dataset search across tabular, spatial, JSON, graph, and vector data, plus the two-way relationship with LLMs.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.