1 citations · 1 across the 3 of their papers we have counts for
6 papers
ExploraTwin, a Non-Profit Research Platform for Digital Twin Simulations
Naveen Venkat, Yuchen Qiu, Tianyi Peng +2
Digital twin simulations show promise, but current empirical evidence suggests that the approach should be tested before being deployed in any particular context. To lower the fric…
Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions
Olivier Toubia, George Z. Gui, Tianyi Peng +3
LLM-based digital twin simulation, where large language models are used to emulate individual human behavior, holds great promise for research in AI, social science, and digital ex…
How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach
Ayeong Lee, Ethan Che, Tianyi Peng
Chain-of-thought prompting has emerged as a powerful technique for enabling large language models (LLMs) to solve complex reasoning tasks. However, these reasoning chains can be ve…
Data Mixture Optimization: A Multi-fidelity Multi-scale Bayesian Framework
Thomson Yen, Andrew Wei Tung Siah, Haozhe Chen +3
Careful curation of data sources can significantly improve the performance of LLM pre-training, but predominant approaches rely heavily on intuition or costly trial-and-error, maki…
LLM Generated Persona is a Promise with a Catch
Ang Li, Haozhe Chen, Hongseok Namkoong +1
The use of large language models (LLMs) to simulate human behavior has gained significant attention, particularly through personas that approximate individual characteristics. Pers…
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Haozhe Chen, Ang Li, Ethan Che +3
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare. Compared to stan…