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Deyang Li

2 accepted papers

2025

Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot

EMNLP 2025

In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce CoT to exemplars of ICL to enhance the reasoning capability, especially in mathematics tasks. However, given the continuous advancement of model capabilities, it remains unclear w

Cited by 0SourcePDFScholar
2025

Reward Mixology: Crafting Hybrid Signals for Reinforcement Learning Driven In-Context Learning

EMNLP 2025

In-context learning (ICL) performance heavily relies on the quality and ordering of demonstrations. Iterative selection (IS) is a promising approach to address this issue, but existing IS methods face two key challenges: the oversimplification of process reward signals that guide intermediate steps

Cited by 0SourcePDFScholar