新闻
FERN: Cross-Benchmark Table Retrieval for Text-to-SQL with Frozen Retrievers and Learned Ranking
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- FERN combines frozen retrievers and learned ranking to retrieve relevant tables for text-to-SQL queries across multiple benchmarks without using generative LLMs.
- 为何重要
- Matters for engineers building text-to-SQL systems over large table catalogs who need fast, efficient table retrieval without expensive model calls.
- 注意
- Performance varies significantly across benchmarks; FERN underperforms on BEAVER and Fiben datasets, suggesting generalization challenges across different table catalog distributions.
- llm
- retrieval
- retriever
- eval
- benchmark
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