9 citations · 16 across the 16 of their papers we have counts for
4 papers · 1 filter
CLOMO: Counterfactual Logical Modification with Large Language Models
Yinya Huang, Ruixin Hong, Hongming Zhang +6
In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs). Our primary objective is to cultivate the counterfactual thought pr…
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations
Zhicheng Yang, Yinya Huang, Jing Xiong +4
Large Language Models prompting, such as using in-context demonstrations, is a mainstream technique for invoking LLMs to perform high-performance and solid complex reasoning (e.g.,…
LEGO-Prover: Neural Theorem Proving with Growing Libraries
Haiming Wang, Huajian Xin, Chuanyang Zheng +11
Despite the success of large language models (LLMs), the task of theorem proving still remains one of the hardest reasoning tasks that is far from being fully solved. Prior methods…
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models
Jing Xiong, Jianhao Shen, Ye Yuan +11
Automated theorem proving (ATP) has become an appealing domain for exploring the reasoning ability of the recent successful generative language models. However, current ATP benchma…