6 papers
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…
Navigating the Shift: A Comparative Analysis of Web Search and Generative AI Response Generation
Mahe Chen, Xiaoxuan Wang, Kaiwen Chen +1
The rise of generative AI as a primary information source presents a paradigm shift from traditional web search. This paper presents a large-scale empirical study quantifying the f…
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…
Generative Engine Optimization: How to Dominate AI Search
Mahe Chen, Xiaoxuan Wang, Kaiwen Chen +1
The rapid adoption of generative AI-powered search engines like ChatGPT, Perplexity, and Gemini is fundamentally reshaping information retrieval, moving from traditional ranked lis…
WeShap: Weak Supervision Source Evaluation with Shapley Values
Naiqing Guan, Nick Koudas
Efficient data annotation stands as a significant bottleneck in training contemporary machine learning models. The Programmatic Weak Supervision (PWS) pipeline presents a solution…
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…