74 citations · 101 across the 10 of their papers we have counts for
10 papers
READY or Not: Reliable Enterprise Agent Deployment
Veronica Chatrath, Bryan Zhu, Jingxuan Fan +15
An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete realistic professional work, w…
CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR
Veronica Chatrath, Bryan Zhu, George Pu +16
Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, lo…
LHAW: Controllable Underspecification for Long-Horizon Tasks
George Pu, Michael S. Lee, Udari Madhushani Sehwag +6
Long-horizon workflow agents that operate effectively over extended periods are essential for truly autonomous systems. Their reliable execution critically depends on the ability t…
Assessing Robustness to Spurious Correlations in Post-Training Language Models
Julia Shuieh, Prasann Singhal, Apaar Shanker +3
Supervised and preference-based fine-tuning techniques have become popular for aligning large language models (LLMs) with user intent and correctness criteria. However, real-world…
Balancing Cost and Effectiveness of Synthetic Data Generation Strategies for LLMs
Yung-Chieh Chan, George Pu, Apaar Shanker +4
As large language models (LLMs) are applied to more use cases, creating high quality, task-specific datasets for fine-tuning becomes a bottleneck for model improvement. Using high…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…
"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters
Yixin Wan, George Pu, Jiao Sun +3
Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content, including professional documents such as recommen…
Server Averaging for Federated Learning
George Pu, Yanlin Zhou, Dapeng Wu +1
Federated learning allows distributed devices to collectively train a model without sharing or disclosing the local dataset with a central server. The global model is optimized by…
Distilled One-Shot Federated Learning
Yanlin Zhou, George Pu, Xiyao Ma +2
Current federated learning algorithms take tens of communication rounds transmitting unwieldy model weights under ideal circumstances and hundreds when data is poorly distributed.…
Adaptive Leader-Follower Formation Control and Obstacle Avoidance via Deep Reinforcement Learning
Yanlin Zhou, Fan Lu, George Pu +5
We propose a deep reinforcement learning (DRL) methodology for the tracking, obstacle avoidance, and formation control of nonholonomic robots. By separating vision-based control in…