Alignment Problem
The alignment problem in artificial intelligence focuses on ensuring that advanced models, particularly large language models (LLMs) and diffusion models, behave in ways consistent with human values and intentions. Current research emphasizes improving reward models, developing more robust evaluation metrics (moving beyond deterministic point estimates to probabilistic frameworks), and exploring various alignment techniques, including preference optimization, knowledge distillation, and contrastive learning, often applied within fine-tuning or training-free frameworks. Successfully addressing the alignment problem is crucial for the safe and ethical deployment of powerful AI systems across diverse applications, ranging from healthcare and drug discovery to robotics and social media moderation.
Papers
AlignXIE: Improving Multilingual Information Extraction by Cross-Lingual Alignment
Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversity
Sonia K. Murthy, Tomer Ullman, Jennifer Hu
OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models
Junda Wu, Xintong Li, Ruoyu Wang, Yu Xia, Yuxin Xiong, Jianing Wang, Tong Yu, Xiang Chen, Branislav Kveton, Lina Yao, Jingbo Shang, Julian McAuley
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
Weichao Zhou, Wenchao Li
BEVPose: Unveiling Scene Semantics through Pose-Guided Multi-Modal BEV Alignment
Mehdi Hosseinzadeh, Ian Reid
SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
Wei Ai, Yinghui Gao, Jianbin Li, Jiayi Du, Tao Meng, Yuntao Shou, Keqin Li
Faster WIND: Accelerating Iterative Best-of-$N$ Distillation for LLM Alignment
Tong Yang, Jincheng Mei, Hanjun Dai, Zixin Wen, Shicong Cen, Dale Schuurmans, Yuejie Chi, Bo Dai