Search Results for author:
Found 4 papers, 1 papers with code
PET-SQL: A Prompt-enhanced Two-stage Text-to-SQL Framework with Cross-consistency
Then, in the first stage, question-SQL pairs are retrieved as few-shot demonstrations, prompting the LLM to generate a preliminary SQL (PreSQL).
Ranked #1 on
Text-To-SQL
on spider
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation
Then we formulate five evaluation tasks to comprehensively assess the performance of diverse methods across various LLMs throughout the Text-to-SQL process. Our study highlights the performance disparities among LLMs and proposes optimal in-context learning solutions tailored to each task.
Spatial-Temporal Large Language Model for Traffic Prediction
In this paper, we propose a Spatial-Temporal Large Language Model (ST-LLM) for traffic prediction.
Reboost Large Language Model-based Text-to-SQL, Text-to-Python, and Text-to-Function -- with Real Applications in Traffic Domain
Our experiments with Large Language Models (LLMs) illustrate the significant performance improvement on the business dataset and prove the substantial potential of our method.
