9 citations · 10 across the 8 of their papers we have counts for
11 papers
Location, Location, Generation: Where You Ask Matters for Generative Engine Optimization
Mahe Chen, Nick Koudas
Generative search engines answer local and commercial questions with a written answer and a few cited sources. A growing practice of generative engine optimization (GEO) monitors w…
Photometric Identification and Dynamical Purification of Blue Horizontal Branch Stars in DESI MWS DR1
Shuai Zhang, Xiaolong Wang, Kai Xiao +5
Blue horizontal-branch (BHB) stars are ideal tracers for mapping the structure of the Galactic stellar halo. However, traditional spectroscopic identification is limited by data qu…
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…
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…
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…
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs
Sana Ebrahimi, Kaiwen Chen, Abolfazl Asudeh +2
Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading…
ActiveDP: Bridging Active Learning and Data Programming
Naiqing Guan, Nick Koudas
Modern machine learning models require large labelled datasets to achieve good performance, but manually labelling large datasets is expensive and time-consuming. The data programm…
Can Large Language Models Design Accurate Label Functions?
Naiqing Guan, Kaiwen Chen, Nick Koudas
Programmatic weak supervision methodologies facilitate the expedited labeling of extensive datasets through the use of label functions (LFs) that encapsulate heuristic data sources…