Zero-to-strong generalization: eliciting strong capabilities of large language models iteratively without gold labels

• Main Authors: Liu, Chaoqun, Chao, Qin, Zhang, Wenxuan, Wu, Xiaobao, Li, Boyang, Luu, Anh Tuan, Bing, Lidong
• 其他作者: Interdisciplinary Graduate School (IGS)
• 格式: Conference or Workshop Item
• 語言: English
• 出版: 2024
• 主題: Computer and Information Science; Zero-to-strong; Weak-to-strong
• 在線閱讀: https://hdl.handle.net/10356/181455; https://coling2025.org/
• 機構: Nanyang Technological University

Large Language Models (LLMs) have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels. However, this paradigm is limited by the availability of gold labels, while in certain scenarios, LLMs may need to perform tasks that are too complex for hum...