1 code implementation • Findings (NAACL) 2022 • Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan
In this paper, we study the challenge of analytical reasoning of text and collect a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016.
1 code implementation • 6 Mar 2024 • Zexuan Qiu, Jingjing Li, Shijue Huang, Wanjun Zhong, Irwin King
Developing Large Language Models (LLMs) with robust long-context capabilities has been the recent research focus, resulting in the emergence of long-context LLMs proficient in Chinese.
no code implementations • 26 Feb 2024 • Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang, Baojun Wang, Wanjun Zhong, Zezhong Wang, Kam-Fai Wong
This dataset is collected to investigate the use of personalized memories, focusing on social interactions and events in the QA task.
1 code implementation • 19 Feb 2024 • Yuxin Jiang, YuFei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang
Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention.
1 code implementation • 30 Jan 2024 • Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.
no code implementations • 28 Jan 2024 • Jianqiao Lu, Wanjun Zhong, YuFei Wang, Zhijiang Guo, Qi Zhu, Wenyong Huang, Yanlin Wang, Fei Mi, Baojun Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu
With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.
1 code implementation • 18 Dec 2023 • Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, YuFei Wang, Lanqing Hong, Jianhua Han, Hang Xu, Zhenguo Li, Lingpeng Kong
We first analyze the limitations of current Multimodal Large Language Models (MLLMs) in this area: they struggle to accurately comprehending basic geometric elements and their relationships.
1 code implementation • 4 Dec 2023 • Zige Wang, Wanjun Zhong, YuFei Wang, Qi Zhu, Fei Mi, Baojun Wang, Lifeng Shang, Xin Jiang, Qun Liu
Data plays a fundamental role in the training of Large Language Models (LLMs).
1 code implementation • 31 Oct 2023 • Yuxin Jiang, YuFei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, Wei Wang
To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs.
1 code implementation • 13 Oct 2023 • Qiming Bao, Gael Gendron, Alex Yuxuan Peng, Wanjun Zhong, Neset Tan, Yang Chen, Michael Witbrock, Jiamou Liu
Despite their high performance on the original publicly available datasets, we find that all models perform poorly on these newly constructed datasets.
no code implementations • 1 Oct 2023 • Jianpeng Zhou, Wanjun Zhong, Yanlin Wang, Jiahai Wang
Experimental results from complex reasoning tasks reveal that the prompting method adaptation and decomposition granularity adaptation enhance performance across all tasks.
no code implementations • 1 Oct 2023 • Jianqiao Lu, Wanjun Zhong, Wenyong Huang, YuFei Wang, Qi Zhu, Fei Mi, Baojun Wang, Weichao Wang, Xingshan Zeng, Lifeng Shang, Xin Jiang, Qun Liu
SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.
1 code implementation • 19 Sep 2023 • Qiming Bao, Juho Leinonen, Alex Yuxuan Peng, Wanjun Zhong, Gaël Gendron, Timothy Pistotti, Alice Huang, Paul Denny, Michael Witbrock, Jiamou Liu
When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts.
1 code implementation • 24 Jul 2023 • YuFei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, Qun Liu
(2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment.
1 code implementation • 27 Jun 2023 • Zhijian Hou, Lei Ji, Difei Gao, Wanjun Zhong, Kun Yan, Chao Li, Wing-Kwong Chan, Chong-Wah Ngo, Nan Duan, Mike Zheng Shou
Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data.
1 code implementation • 21 May 2023 • Qiming Bao, Alex Yuxuan Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner.
1 code implementation • 17 May 2023 • Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, Yanlin Wang
To mimic anthropomorphic behaviors and selectively preserve memory, MemoryBank incorporates a memory updating mechanism, inspired by the Ebbinghaus Forgetting Curve theory, which permits the AI to forget and reinforce memory based on time elapsed and the relative significance of the memory, thereby offering a human-like memory mechanism.
2 code implementations • 13 Apr 2023 • Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan
Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92. 5% accuracy on the English test of the Chinese national college entrance exam.
no code implementations • 16 Nov 2022 • Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, Wing-Kwong Chan, Chong-Wah Ngo, Zheng Shou, Nan Duan
This technical report describes the CONE approach for Ego4D Natural Language Queries (NLQ) Challenge in ECCV 2022.
no code implementations • 20 Oct 2022 • Wanjun Zhong, Tingting Ma, Jiahai Wang, Jian Yin, Tiejun Zhao, Chin-Yew Lin, Nan Duan
This paper presents ReasonFormer, a unified reasoning framework for mirroring the modular and compositional reasoning process of humans in complex decision-making.
1 code implementation • 11 Oct 2022 • JunJie Huang, Wanjun Zhong, Qian Liu, Ming Gong, Daxin Jiang, Nan Duan
However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem.
1 code implementation • 22 Sep 2022 • Zhijian Hou, Wanjun Zhong, Lei Ji, Difei Gao, Kun Yan, Wing-Kwong Chan, Chong-Wah Ngo, Zheng Shou, Nan Duan
This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query.
no code implementations • 5 Aug 2022 • Wanjun Zhong, Yifan Gao, Ning Ding, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan
Task generalization has been a long standing challenge in Natural Language Processing (NLP).
1 code implementation • 18 May 2022 • Xinyu Pi, Wanjun Zhong, Yan Gao, Nan Duan, Jian-Guang Lou
We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models.
no code implementations • 13 May 2022 • Zenan Xu, Wanjun Zhong, Qinliang Su, Zijing Ou, Fuwei Zhang
A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects.
1 code implementation • NAACL 2022 • Wanjun Zhong, Yifan Gao, Ning Ding, Yujia Qin, Zhiyuan Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan
Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.
no code implementations • 15 Jan 2022 • Wanjun Zhong, JunJie Huang, Qian Liu, Ming Zhou, Jiahai Wang, Jian Yin, Nan Duan
CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.
Ranked #2 on Question Answering on OTT-QA
1 code implementation • 2 Aug 2021 • Siyuan Wang, Zhongkun Liu, Wanjun Zhong, Ming Zhou, Zhongyu Wei, Zhumin Chen, Nan Duan
Complex reasoning aims to draw a correct inference based on complex rules.
1 code implementation • ACL 2021 • Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, Ming Zhou
Specifically, we first construct a \textit{directed heterogeneous document graph} for each news incorporating topics and entities.
2 code implementations • Findings (ACL) 2022 • Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan
Logical reasoning of text requires understanding critical logical information in the text and performing inference over them.
Ranked #7 on Reading Comprehension on ReClor
1 code implementation • 14 Apr 2021 • Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan
Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to make conclusions.
1 code implementation • Findings (EMNLP) 2021 • JunJie Huang, Duyu Tang, Wanjun Zhong, Shuai Lu, Linjun Shou, Ming Gong, Daxin Jiang, Nan Duan
In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings.
1 code implementation • ACL 2021 • Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Nan Duan, Daxin Jiang
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa.
1 code implementation • EMNLP 2020 • Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin
To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text.
no code implementations • EMNLP 2020 • Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong, Zhongyu Wei, Xuanjing Huang, Daxin Jiang, Ming Zhou
We study the detection of propagandistic text fragments in news articles.
no code implementations • ACL 2020 • Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin
The graph is used to obtain graph-enhanced contextual representations of words in Transformer-based architecture.
no code implementations • 25 Apr 2020 • Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin
We study question answering over a dynamic textual environment.
no code implementations • ACL 2020 • Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin
We evaluate our system on FEVER, a benchmark dataset for fact checking, and find that rich structural information is helpful and both our graph-based mechanisms improve the accuracy.
Ranked #2 on Fact Verification on FEVER
no code implementations • 5 Sep 2018 • Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin
Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge.