26 citations · 46 across the 11 of their papers we have counts for
11 papers
SubgoalXL: Subgoal-based Expert Learning for Theorem Proving
Xueliang Zhao, Lin Zheng, Haige Bo +3
Formal theorem proving, a field at the intersection of mathematics and computer science, has seen renewed interest with advancements in large language models (LLMs). This paper int…
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving
Xueliang Zhao, Xinting Huang, Wei Bi +1
Large Language Models (LLMs) have driven substantial progress in artificial intelligence in recent years, exhibiting impressive capabilities across a wide range of tasks, including…
Extrapolating Large Language Models to Non-English by Aligning Languages
Wenhao Zhu, Yunzhe Lv, Qingxiu Dong +6
Existing large language models show disparate capability across different languages, due to the imbalance in the training data. Their performances on English tasks are often strong…
Language Versatilists vs. Specialists: An Empirical Revisiting on Multilingual Transfer Ability
Jiacheng Ye, Xijia Tao, Lingpeng Kong
Multilingual transfer ability, which reflects how well the models fine-tuned on one source language can be applied to other languages, has been well studied in multilingual pre-tra…
Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving
Xueliang Zhao, Wenda Li, Lingpeng Kong
Large language models~(LLMs) present an intriguing avenue of exploration in the domain of formal theorem proving. Nonetheless, the full utilization of these models, particularly in…
Can Language Models Understand Physical Concepts?
Lei Li, Jingjing Xu, Qingxiu Dong +4
Language models~(LMs) gradually become general-purpose interfaces in the interactive and embodied world, where the understanding of physical concepts is an essential prerequisite.…