3 papers
cs.AI2026
Process In-Context Learning: Enhancing Mathematical Reasoning via Dynamic Demonstration Insertion
Ang Gao, Changshuo Zhang, Xiao Zhang +4
In-context learning (ICL) has proven highly effective across diverse large language model (LLM) tasks. However, its potential for enhancing tasks that demand step-by-step logical d…
cs.CL2025
ADOPT: Adaptive Dependency-Guided Joint Prompt Optimization for Multi-Step LLM Pipelines
Minjun Zhao, Xinyu Zhang, Shuai Zhang +2
Multi-step LLM pipelines can solve complex tasks, but jointly optimizing prompts across steps remains challenging due to missing step-level supervision and inter-step dependency. W…
cs.CL2025
Revisiting Chain-of-Thought Prompting: Zero-shot Can Be Stronger than Few-shot
Xiang Cheng, Chengyan Pan, Minjun Zhao +5
In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce Chain-of-Thought (CoT) to exemplars of ICL to enhance the r…