15 papers
Large Language Models Develop Novel Social Biases Through Adaptive Exploration
Addison J. Wu, Ryan Liu, Xuechunzi Bai +1
As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In t…
Enhancing the MADDPG Algorithm for Multi-Agent Learning via Action Inference and Importance Sampling
Marc Walden, Jason Liu, Shaashwath Sivakumar +2
We investigate multi-agent deep reinforcement learning and propose two enhancements to the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. First, we introduce a…
Evaluating Language Models' Evaluations of Games
Katherine M. Collins, Cedegao E. Zhang, Graham Todd +9
Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial intelligence (AI) systems primarily f…
Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest
Addison J. Wu, Ryan Liu, Shuyue Stella Li +2
Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning. Yet models are beginning to be deployed not solely to satisfy u…
Levels of Analysis for Large Language Models
Alexander Y. Ku, Declan Campbell, Xuechunzi Bai +10
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to…
WaterVideoQA: ASV-Centric Perception and Rule-Compliant Reasoning via Multi-Modal Agents
Runwei Guan, Shaofeng Liang, Ningwei Ouyang +9
While autonomous navigation has achieved remarkable success in passive perception (e.g., object detection and segmentation), it remains fundamentally constrained by a void in knowl…