Access Paths for Efficient Ordering with Large Language Models

arXiv:2509.00303 · cs.DB, cs.AI, cs.IR · Submitted 2025-08-30 · Read on arXiv

cs.DB, cs.AI, cs.IR

Submitted: 2025-08-30

Updated: 2026-09-14

Code: https://github.com/BerriAI/litellm

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 70× in this paper · explained in 1 episode
• Benchmark — 18× in this paper · explained in 2 episodes
• Consistency — 18× in this paper · explained in 3 episodes
• Self-consistency — 18× in this paper · explained in 2 episodes
• Aggregation — 10× in this paper · explained in 1 episode
• LLM-as-a-Judge — 5× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Test-time Scaling — 3× in this paper · explained in 2 episodes
• Text-to-SQL — 3× in this paper · explained in 3 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Conference on Innovative Database Research — 1× in this paper
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Cortex AISQL: A Production SQL Engine for Unstructured Data
• Batch Prompting: Efficient Inference with Large Language Model APIs
• Overview of the TREC 2019 deep learning track
• PaRaDe: Passage Ranking using Demonstrations with Large Language Models
• Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
• BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra
• SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL
• A Survey on LLM-as-a-Judge
• Noise Stability of Ranked Choice Voting
• FLEX: Expert-level False-Less EXecution Metric for Reliable Text-to-SQL Benchmark
• SemBench: A Benchmark for Semantic Query Processing Engines
• Holistic Evaluation of Language Models
• Visual Template Inference for Data Extraction from Documents
• Zero-Shot Listwise Document Reranking with a Large Language Model
• How much do language models memorize?
• Document Ranking with a Pretrained Sequence-to-Sequence Model
• Deep sequence models tend to memorize geometrically; it is unclear why
• RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
• Improving Passage Retrieval with Zero-Shot Question Generation

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