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cs.DB2026
Can we trust LLM Self-Explanations for Entity Resolution?
Tommaso Teofili, Donatella Firmani, Nick Koudas +2
Large Language Models (LLMs) have recently shown strong performance on Entity Resolution (ER). Additionally, akin to their prowess in providing accurate predictions, these models o…
cs.DB2025
Relational Deep Dive: Error-Aware Queries Over Unstructured Data
Daren Chao, Kaiwen Chen, Naiqing Guan +1
Unstructured data is pervasive, but analytical queries demand structured representations, creating a significant extraction challenge. Existing methods like RAG lack schema awarene…
cs.DB2025
Reliable Text-to-SQL with Adaptive Abstention
Kaiwen Chen, Yueting Chen, Xiaohui Yu +1
Large language models (LLMs) have revolutionized natural language interfaces for databases, particularly in text-to-SQL conversion. However, current approaches often generate unrel…