Zero-to-strong generalization: eliciting strong capabilities of large language models iteratively without gold labels
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...
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Main Authors: | Liu, Chaoqun, Chao, Qin, Zhang, Wenxuan, Wu, Xiaobao, Li, Boyang, Luu, Anh Tuan, Bing, Lidong |
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Other Authors: | Interdisciplinary Graduate School (IGS) |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/181455 https://coling2025.org/ |
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Institution: | Nanyang Technological University |
Language: | English |
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