no code implementations • 15 Nov 2023 • Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language(e. g., chemical molecular formula).
1 code implementation • 9 Oct 2023 • Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria
This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to datacentered methodologies.
1 code implementation • 16 Jun 2023 • Fangzhi Xu, Qika Lin, Jiawei Han, Tianzhe Zhao, Jun Liu, Erik Cambria
Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings.
1 code implementation • 8 Jan 2023 • Fangzhi Xu, Jun Liu, Qika Lin, Tianzhe Zhao, Jian Zhang, Lingling Zhang
(2) How to enhance the perception of reasoning types for the models?
1 code implementation • 2 May 2022 • Fangzhi Xu, Jun Liu, Qika Lin, Yudai Pan, Lingling Zhang
Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively.
Ranked #14 on Reading Comprehension on ReClor
no code implementations • 6 Dec 2021 • Fangzhi Xu, Qika Lin, Jun Liu, Lingling Zhang, Tianzhe Zhao, Qi Chai, Yudai Pan
Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams.
no code implementations • 17 Oct 2021 • Yudai Pan, Jun Liu, Lingling Zhang, Xin Hu, Tianzhe Zhao, Qika Lin
Relation reasoning in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm is learning the embeddings of relations and entities, which is limited to a transductive setting and has restriction on processing unseen entities in an inductive situation.