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cs.CL2025
Understanding Emergent In-Context Learning from a Kernel Regression Perspective
Chi Han, Ziqi Wang, Han Zhao +1
Large language models (LLMs) have initiated a paradigm shift in transfer learning. In contrast to the classic pretraining-then-finetuning procedure, in order to use LLMs for downst…
cs.CL2024
Enabling Language Models to Implicitly Learn Self-Improvement
Ziqi Wang, Le Hou, Tianjian Lu +4
Large Language Models (LLMs) have demonstrated remarkable capabilities in open-ended text generation tasks. However, the inherent open-ended nature of these tasks implies that ther…
cs.CL2024
PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning
Tianci Xue, Ziqi Wang, Yixia Li +2
Instruction tuning enhances the instruction following ability of large language models by finetuning with supervised instruction data. Previous work proposes in-context instruction…