Human Feedback
Human feedback is crucial for aligning artificial intelligence models, particularly large language models, with human preferences and values. Current research focuses on improving the efficiency and reliability of incorporating human feedback into reinforcement learning frameworks, exploring techniques like macro actions, active learning, and reward model optimization to address challenges such as the cost and subjectivity of human judgments. This work is significant because it directly impacts the safety, trustworthiness, and overall effectiveness of AI systems across diverse applications, from autonomous driving to educational assessment. The development of more robust and efficient methods for integrating human feedback is a key area of ongoing investigation.
Papers
Preemptive Detection and Correction of Misaligned Actions in LLM Agents
Haishuo Fang, Xiaodan Zhu, Iryna Gurevych
FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models
Pengxiang Li, Zhi Gao, Bofei Zhang, Tao Yuan, Yuwei Wu, Mehrtash Harandi, Yunde Jia, Song-Chun Zhu, Qing Li
Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis
Qining Zhang, Honghao Wei, Lei Ying
Joint Learning of Context and Feedback Embeddings in Spoken Dialogue
Livia Qian, Gabriel Skantze
Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment
Chenliang Li, Siliang Zeng, Zeyi Liao, Jiaxiang Li, Dongyeop Kang, Alfredo Garcia, Mingyi Hong