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Found 7 papers, 3 papers with code
Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates
The steam drum water level is a critical parameter that directly impacts the safety and efficiency of power plant operations.
TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment
As another key design, to reduce the computational costs from time series with their length textual prompts, we design an effective prompt to encourage the most essential temporal information to be encapsulated in the last token: only the last token is passed to downstream prediction.
Ranked #8 on
Time Series Forecasting
on ETTh1 (336) Multivariate
SQL-to-Schema Enhances Schema Linking in Text-to-SQL
Consequently, there is a critical need to filter out unnecessary tables and columns, directing the language models attention to relevant tables and columns with schema-linking, to reduce errors during SQL generation.
PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL 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 #2 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 the ST-LLM, we define timesteps at each location as tokens and design a spatial-temporal embedding to learn the spatial location and global temporal patterns of these tokens.
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.
