28 citations · 151 across the 19 of their papers we have counts for
19 papers
Revisiting Human-vs-LLM judgments using the TREC Podcast Track
Watheq Mansour, J. Shane Culpepper, Joel Mackenzie +1
Using large language models (LLMs) to annotate relevance is an increasingly important technique in the information retrieval community. While some studies demonstrate that LLMs can…
The Effects of Demographic Instructions on LLM Personas
Angel Felipe Magnossão de Paula, J. Shane Culpepper, Alistair Moffat +3
Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized…
Distinctiveness Maximization in Datasets Assemblage
Tingting Wang, Shixun Huang, Zhifeng Bao +3
In this paper, given a user's query set and budget, we aim to use the limited budget to help users assemble a set of datasets that can enrich a base dataset by introducing the maxi…
A Simple Yet High-Performing On-disk Learned Index: Can We Have Our Cake and Eat it Too?
Hai Lan, Zhifeng Bao, J. Shane Culpepper +2
While in-memory learned indexes have shown promising performance as compared to B+-tree, most widely used databases in real applications still rely on disk-based operations. Based…
Updatable Learned Indexes Meet Disk-Resident DBMS -- From Evaluations to Design Choices
Hai Lan, Zhifeng Bao, J. Shane Culpepper +1
Although many updatable learned indexes have been proposed in recent years, whether they can outperform traditional approaches on disk remains unknown. In this study, we revisit an…
Let Trajectories Speak Out the Traffic Bottlenecks
Hui Luo, Zhifeng Bao, Gao Cong +2
Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajec…