6 citations · 11 across the 14 of their papers we have counts for
8 papers · 1 filter
Accumulating Context Changes the Beliefs of Language Models
Jiayi Geng, Howard Chen, Ryan Liu +4
Language model (LM) assistants are increasingly used in applications such as brainstorming and research. Improvements in memory and context size have allowed these models to become…
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…
Are Large Language Models Sensitive to the Motives Behind Communication?
Addison J. Wu, Ryan Liu, Kerem Oktar +2
Human communication is motivated: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs)…
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…
Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks
Yuxiang Wei, Zhuoqi Zeng, Yue Zhong +3
With the advancement of large language models and embodied Artificial Intelligence (AI) in the intelligent transportation scenarios, the combination of them in intelligent transpor…
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…
On Benchmarking Human-Like Intelligence in Machines
Lance Ying, Katherine M. Collins, Lionel Wong +6
Recent advances in Artificial Intelligence (AI) have yielded powerful computational models that, by learning from vast amounts of human-generated data, are increasingly posited as…
RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
Kaiqu Liang, Haimin Hu, Ryan Liu +2
While Reinforcement Learning from Human Feedback (RLHF) has shown promise in aligning generative AI, we present empirical evidence that it can also cause severe, systematic misalig…