8 papers
Prompt Injection in Automated Résumé Screening with Large Language Models: Single and Multi-Injection Settings
Preet Baxi, Jiannan Xu, Jane Yi Jiang +1
Large language models (LLMs) are increasingly used to screen and rank job applicants, creating incentives for candidates to strategically manipulate algorithmic hiring systems. We…
From Small to Large: A Graph Convolutional Network Approach for Solving Assortment Optimization Problems
Guokai Li, Pin Gao, Stefanus Jasin +1
Assortment optimization seeks to select a subset of substitutable products, subject to constraints, to maximize expected revenue. The problem is NP-hard due to its combinatorial an…
A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming
Dae Woong Ham, Stefanus Jasin, Xuejun Zhao
Sequential hypothesis tests are widely adopted as a principled way to perform multiple tests on data that arrives over time. In particular, researchers frequently utilize group seq…
Asymptotically Optimal Sequential Testing with Heterogeneous LLMs
Guokai Li, Alys Liang, Mo Liu +4
We study a Bayesian binary sequential hypothesis testing problem with multiple large language models (LLMs). Each LLM has per-query cost , random waiting time with mean…
Multi-LLM Query Optimization
Arlen Dean, Zijin Zhang, Stefanus Jasin +1
Deploying multiple large language models (LLMs) in parallel to classify an unknown ground-truth label is a common practice, yet the problem of optimally allocating queries across h…
Minimizing Type 2 Errors in an Experiment-Rich Regime via Optimal Resource Allocation
Fenghua Yang, Dae Woong Ham, Stefanus Jasin
Randomized experiments (often known as "A/B tests") are widely used to evaluate product and service innovations. We study how to allocate limited experimentation resources across M…