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cs.CL2025
Less Data Less Tokens: Multilingual Unification Learning for Efficient Test-Time Reasoning in LLMs
Kang Chen, Mengdi Zhang, Yixin Cao
This paper explores the challenges of test-time scaling of large language models (LLMs), regarding both the data and inference efficiency. We highlight the diversity of multi-lingu…
cs.CL2024
LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning
Jin Jiang, Yuchen Yan, Yang Liu +6
In this paper, we propose a new data synthesis method called \textbf{LogicPro}, which leverages LeetCode-style algorithm \underline{Pro}blems and their corresponding \underline{Pro…
cs.CL2024
S^3cMath: Spontaneous Step-level Self-correction Makes Large Language Models Better Mathematical Reasoners
Yuchen Yan, Jin Jiang, Yang Liu +5
Self-correction is a novel method that can stimulate the potential reasoning abilities of large language models (LLMs). It involves detecting and correcting errors during the infer…