Search Results for author:
Found 7 papers, 5 papers with code
SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees
SMART employs a profiling phase that evaluates the performance of multiple LLMs to identify those that meet the user-defined accuracy level.
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost and it is the core NP-hard combinatorial optimization problem of query optimization.
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes
Code synthesis is cheap, but far less accurate than directly processing each document with the LLM.
CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex
CodexDB is an SQL processing engine whose internals can be customized via natural language instructions.
DB-BERT: a Database Tuning Tool that "Reads the Manual"
DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents.
Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback
In black-box optimization problems, we aim to maximize an unknown objective function, where the function is only accessible through feedbacks of an evaluation or simulation oracle.
Can Deep Neural Networks Predict Data Correlations from Column Names?
This paper examines that hypothesis in the context of data correlation analysis: is it possible to find column pairs with correlated data by analyzing their names via language models?
