8 papers · 1 filter
ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization
Jamison Meindl, Yunsheng Tian, Tony Cui +6
Global optimization of expensive, derivative-free black-box functions requires extreme sample efficiency. While Bayesian optimization (BO) is the current state-of-the-art, its perf…
SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization
Jamison Meindl, Yunsheng Tian, Tony Cui +5
Optimizing an experimental system can be extremely challenging when each experiment is expensive, time-consuming, or difficult to perform. Existing optimizers for expensive black-b…
Neighborhood Sampling Does Not Learn the Same Graph Neural Network
Zehao Niu, Mihai Anitescu, Jie Chen
Neighborhood sampling is an important ingredient in the training of large-scale graph neural networks. It suppresses the exponential growth of the neighborhood size across network…
Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks
Ziyuan Tang, Jie Chen
A foundation model like GPT elicits many emergent abilities, owing to the pre-training with broad inclusion of data and the use of the powerful Transformer architecture. While foun…
Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for Reasoning
Jie Cheng, Gang Xiong, Ruixi Qiao +5
Process reward models (PRMs) have proven effective for test-time scaling of Large Language Models (LLMs) on challenging reasoning tasks. However, reward hacking issues with PRMs li…
Model Risk Management for Generative AI In Financial Institutions
Anwesha Bhattacharyya, Ye Yu, Hanyu Yang +4
The success of OpenAI's ChatGPT in 2023 has spurred financial enterprises into exploring Generative AI applications to reduce costs or drive revenue within different lines of busin…
Exploring Large Language Models for Word Games:Who is the Spy?
Chentian Wei, Jiewei Chen, Jinzhu Xu
Word games hold significant research value for natural language processing (NLP), game theory, and related fields due to their rule-based and situational nature. This study explore…
Teaching Language Models to Critique via Reinforcement Learning
Zhihui Xie, Jie Chen, Liyu Chen +3
Teaching large language models (LLMs) to critique and refine their outputs is crucial for building systems that can iteratively improve, yet it is fundamentally limited by the abil…