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Found 5 papers, 2 papers with code
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics.
Ranked #1 on
Text-To-SQL
on Spider 2.0
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.
End-to-End Argument Mining as Biaffine Dependency Parsing
Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering.
Improving Cross-Domain Chinese Word Segmentation with Word Embeddings
Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS.
