1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
WhatIf: Interactive Exploration of Large-Scale LLM Agent Simulations for Policy Reasoning
Yuxuan Li, Kyzyl Monteiro, Hirokazu Shirado +1
Policymakers in domains such as emergency management, public health, and urban planning must make decisions under deep uncertainty, where outcomes depend on how large populations i…
What Makes LLM Agent Simulations Useful for Policy Practice? An Iterative Design Study in Emergency Preparedness
Yuxuan Li, Sauvik Das, Hirokazu Shirado
Policymakers must often act under conditions of deep uncertainty, such as emergency response, where predicting the specific impacts of a policy apriori is implausible. Large Langua…
Say Your Reason: Extract Contextual Rules In Situ for Context-aware Service Recommendation
Yuxuan Li, Jiahui Li, Lihang Pan +2
This paper introduces SayRea, an interactive system that facilitates the extraction of contextual rules for personalized context-aware service recommendations in mobile scenarios.…
A Human-Computer Collaborative Tool for Training a Single Large Language Model Agent into a Network through Few Examples
Lihang Pan, Yuxuan Li, Chun Yu +1
The capabilities of a single large language model (LLM) agent for solving a complex task are limited. Connecting multiple LLM agents to a network can effectively improve overall pe…