no code implementations • 29 Nov 2023 • Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, ChangShui Zhang, Xiaodan Liang, Linqi Song
In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs).
1 code implementation • 14 Nov 2023 • Ruixin Hong, Hongming Zhang, Xinyu Pang, Dong Yu, ChangShui Zhang
In this paper, we take a closer look at the self-verification abilities of LLMs in the context of logical reasoning, focusing on their ability to identify logical fallacies accurately.
1 code implementation • 4 May 2023 • Ruixin Hong, Hongming Zhang, Hong Zhao, Dong Yu, ChangShui Zhang
In this paper, we propose FAME (FAithful question answering with MontE-carlo planning) to answer questions based on faithful reasoning steps.
no code implementations • 22 Mar 2023 • Zhilong Liang, Zhenzhi Tan, Ruixin Hong, Wanli Ouyang, Jinying Yuan, ChangShui Zhang
Computer image recognition with machine learning method can make up the defects of artificial judging, giving accurate and quantitative judgement.
3 code implementations • 22 Oct 2022 • Yinya Huang, Hongming Zhang, Ruixin Hong, Xiaodan Liang, ChangShui Zhang, Dong Yu
To this end, we propose a comprehensive logical reasoning explanation form.
1 code implementation • 29 May 2022 • Xintong Yu, Hongming Zhang, Ruixin Hong, Yangqiu Song, ChangShui Zhang
In this paper, we propose VD-PCR, a novel framework to improve Visual Dialog understanding with Pronoun Coreference Resolution in both implicit and explicit ways.
3 code implementations • Findings (NAACL) 2022 • Ruixin Hong, Hongming Zhang, Xintong Yu, ChangShui Zhang
Advances on QA explanation propose to explain the answers with entailment trees composed of multiple entailment steps.