10 citations · 35 across the 11 of their papers we have counts for
6 papers · 1 filter
Emotional Intelligence of Large Language Models
Xuena Wang, Xueting Li, Zi Yin +2
Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and…
One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration
Yongzhe Yuan, Yue Wu, Maoguo Gong +2
The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially ov…
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Junhao Zheng, Qianli Ma, Shengjie Qiu +6
Fine-tuning has been proven to be a simple and effective technique to transfer the learned knowledge of Pre-trained Language Models (PLMs) to downstream tasks. However, vanilla fin…
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents
Yue Wu, So Yeon Min, Yonatan Bisk +5
Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging co…
SPRING: Studying the Paper and Reasoning to Play Games
Yue Wu, Shrimai Prabhumoye, So Yeon Min +5
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement le…
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Yue Wu, Yewen Fan, Paul Pu Liang +3
High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructu…