3 papers
cs.CL2025
RIDE: Difficulty Evolving Perturbation with Item Response Theory for Mathematical Reasoning
Xinyuan Li, Murong Xu, Wenbiao Tao +4
Large language models (LLMs) achieve high performance on mathematical reasoning, but these results can be inflated by training data leakage or superficial pattern matching rather t…
cs.CL2023
R Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context
Qingyuan Tian, Hanlun Zhu, Lei Wang +2
With the help of Chain-of-Thought (CoT) prompting, Large Language Models (LLMs) have achieved remarkable performance on various reasoning tasks. However, most of them have been eva…
cs.CL2023
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation
Yuanyuan Liang, Jianing Wang, Hanlun Zhu +3
The task of Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question. For the sake of expensive cost of large-scale question…