no code implementations • COLING 2022 • Zhichao Geng, Ming Zhong, Zhangyue Yin, Xipeng Qiu, Xuanjing Huang
For dialogue summarization, the subdomain of text summarization, utterances are concatenated to flat text before being processed.
no code implementations • 17 Dec 2024 • Hankun Kang, Jianhao Chen, Yongqi Li, Xin Miao, Mayi Xu, Ming Zhong, Yuanyuan Zhu, Tieyun Qian
While existing methods perform well on normal toxic contents or those generated by specific perturbation methods, they are vulnerable to evolving perturbation patterns.
no code implementations • 27 Oct 2024 • Ming Zhong, Zhizhi Wu, Nanako Honda
Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but their robustness against tokenizer poisoning remains underexplored.
no code implementations • 24 Oct 2024 • Chenxin An, Jun Zhang, Ming Zhong, Lei LI, Shansan Gong, Yao Luo, Jingjing Xu, Lingpeng Kong
Advancements in distributed training and efficient attention mechanisms have significantly expanded the context window sizes of large language models (LLMs).
no code implementations • 14 Oct 2024 • Siru Ouyang, Shuohang Wang, Minhao Jiang, Ming Zhong, Donghan Yu, Jiawei Han, Yelong Shen
This paper delves into the effects of decoding temperatures on speculative decoding's efficacy.
1 code implementation • 30 Sep 2024 • Ming Zhong, Aston Zhang, Xuewei Wang, Rui Hou, Wenhan Xiong, Chenguang Zhu, Zhengxing Chen, Liang Tan, Chloe Bi, Mike Lewis, Sravya Popuri, Sharan Narang, Melanie Kambadur, Dhruv Mahajan, Sergey Edunov, Jiawei Han, Laurens van der Maaten
The development and evaluation of Large Language Models (LLMs) have largely focused on individual capabilities.
no code implementations • 2 Sep 2024 • Jin Song, Ming Zhong, George Em Karniadakis, Zhenya Yan
We propose a new two-stage initial-value iterative neural network (IINN) algorithm for solitary wave computations of nonlinear wave equations based on traditional numerical iterative methods and physics-informed neural networks (PINNs).
1 code implementation • 16 Aug 2024 • Yulong Chen, Yang Liu, Jianhao Yan, Xuefeng Bai, Ming Zhong, Yinghao Yang, ZiYi Yang, Chenguang Zhu, Yue Zhang
We then build a benchmark, SC-G4, consisting of 1, 835 instances generated by GPT-4 using these patterns, with human-annotated gold responses.
1 code implementation • 10 Aug 2024 • Kerui Zhu, Bo-Wei Huang, Bowen Jin, Yizhu Jiao, Ming Zhong, Kevin Chang, Shou-De Lin, Jiawei Han
Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks.
no code implementations • 8 Apr 2024 • Ming Zhong, Dehao Liu, Raymundo Arroyave, Ulisses Braga-Neto
This paper proposes a semi-supervised methodology for training physics-informed machine learning methods.
no code implementations • 26 Feb 2024 • Ming Zhong, Yelong Shen, Shuohang Wang, Yadong Lu, Yizhu Jiao, Siru Ouyang, Donghan Yu, Jiawei Han, Weizhu Chen
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images.
no code implementations • 11 Jan 2024 • Minhao Jiang, Ken Ziyu Liu, Ming Zhong, Rylan Schaeffer, Siru Ouyang, Jiawei Han, Sanmi Koyejo
Language models pre-trained on web-scale corpora demonstrate impressive capabilities on diverse downstream tasks.
no code implementations • 21 Nov 2023 • Ren Wang, Ming Zhong, Kaidi Xu, Lola Giráldez Sánchez-Cortés, Ignacio de Cominges Guerra
This paper addresses the challenge of transient stability in power systems with missing parameters and uncertainty propagation in swing equations.
no code implementations • 1 Nov 2023 • Jinchao Feng, Ming Zhong
We present a comprehensive examination of learning methodologies employed for the structural identification of dynamical systems.
1 code implementation • 24 Oct 2023 • Yizhu Jiao, Ming Zhong, Sha Li, Ruining Zhao, Siru Ouyang, Heng Ji, Jiawei Han
However, when it comes to information extraction - a classic task in natural language processing - most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users.
1 code implementation • 19 Oct 2023 • Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks.
1 code implementation • 17 Oct 2023 • Ming Zhong, Chenxin An, Weizhu Chen, Jiawei Han, Pengcheng He
In this paper, we seek to empirically investigate knowledge transfer from larger to smaller models through a parametric perspective.
3 code implementations • 20 Jul 2023 • Chenxin An, Shansan Gong, Ming Zhong, Xingjian Zhao, Mukai Li, Jun Zhang, Lingpeng Kong, Xipeng Qiu
Recently, there has been growing interest in extending the context length of large language models (LLMs), aiming to effectively process long inputs of one turn or conversations with more extensive histories.
1 code implementation • ICCV 2023 • Bin Duan, Ming Zhong, Yan Yan
Moreover, we derive a set of theoretical guarantees for our sanity-checked image registration method, with experimental results supporting our theoretical findings and their effectiveness in increasing the sanity of models without sacrificing any performance.
1 code implementation • 8 Jul 2023 • Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Michael Zhu, Yue Zhang
Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process.
no code implementations • 4 Jul 2023 • Ming Zhong, Siru Ouyang, Minhao Jiang, Vivian Hu, Yizhu Jiao, Xuan Wang, Jiawei Han
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design.
1 code implementation • 11 Jun 2023 • Ting Zhang, Zhuang Chen, Ming Zhong, Tieyun Qian
It is a challenging task since the recognition of the emotion in one utterance involves many complex factors, such as the conversational context, the speaker's background, and the subtle difference between emotion labels.
1 code implementation • 23 May 2023 • Da Yin, Xiao Liu, Fan Yin, Ming Zhong, Hritik Bansal, Jiawei Han, Kai-Wei Chang
Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses.
1 code implementation • 3 Nov 2022 • Yizhu Jiao, Sha Li, Yiqing Xie, Ming Zhong, Heng Ji, Jiawei Han
Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles.
2 code implementations • 13 Oct 2022 • Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, PengFei Liu, Chenguang Zhu, Heng Ji, Jiawei Han
We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.
1 code implementation • COLING 2022 • Chenxin An, Ming Zhong, Zhiyong Wu, Qin Zhu, Xuanjing Huang, Xipeng Qiu
Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives.
no code implementations • 29 Aug 2022 • Ming Zhong, Zhenya Yan
The results obtained in this paper may be useful to further understand the neural networks in the fractional integrable nonlinear wave systems and the mappings between two spaces.
no code implementations • 4 Aug 2022 • Jinchao Feng, Mauro Maggioni, Patrick Martin, Ming Zhong
Dynamical systems across many disciplines are modeled as interacting particles or agents, with interaction rules that depend on a very small number of variables (e. g. pairwise distances, pairwise differences of phases, etc...), functions of the state of pairs of agents.
1 code implementation • 12 May 2022 • Yuning Mao, Ming Zhong, Jiawei Han
Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers.
Ranked #1 on
Extreme Summarization
on CiteSum
1 code implementation • 5 May 2022 • Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu, Ahmed Hassan Awadallah, Ming Zhong, Milad Shokouhi
The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.
2 code implementations • 1 May 2022 • Yulong Chen, Ming Zhong, Xuefeng Bai, Naihao Deng, Jing Li, Xianchao Zhu, Yue Zhang
We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation.
Abstractive Dialogue Summarization
Conversation Summarization
+4
2 code implementations • 29 Jan 2022 • Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han
In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum.
1 code implementation • 16 Jan 2022 • Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
no code implementations • 29 Sep 2021 • Chang Wan, Yanwei Fu, Ke Fan, Jinshan Zeng, Ming Zhong, Riheng Jia, MingLu Li, ZhongLong Zheng
However, the discriminator using logistic regression from the CFG framework is gradually hard to discriminate between real and fake images while the training steps go on.
no code implementations • 16 Sep 2021 • Chenxin An, Ming Zhong, Zhichao Geng, Jianqiang Yang, Xipeng Qiu
Existing summarization systems mostly generate summaries purely relying on the content of the source document.
1 code implementation • 6 Sep 2021 • Ming Zhong, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng
For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation.
no code implementations • 26 Aug 2021 • Ming Zhong, Jason Miller, Mauro Maggioni
Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics.
1 code implementation • Findings (ACL) 2021 • Lin Su, Nan Duan, Edward Cui, Lei Ji, Chenfei Wu, Huaishao Luo, Yongfei Liu, Ming Zhong, Taroon Bharti, Arun Sacheti
Comparing with existing multimodal datasets such as MSCOCO and Flicker30K for image-language tasks, YouCook2 and MSR-VTT for video-language tasks, GEM is not only the largest vision-language dataset covering image-language tasks and video-language tasks at the same time, but also labeled in multiple languages.
5 code implementations • 18 Apr 2021 • Huaishao Luo, Lei Ji, Ming Zhong, Yang Chen, Wen Lei, Nan Duan, Tianrui Li
In this paper, we propose a CLIP4Clip model to transfer the knowledge of the CLIP model to video-language retrieval in an end-to-end manner.
Ranked #1 on
Text to Video Retrieval
on MSR-VTT
1 code implementation • NAACL 2021 • Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
1 code implementation • 7 Apr 2021 • Chenxin An, Ming Zhong, Yiran Chen, Danqing Wang, Xipeng Qiu, Xuanjing Huang
Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network.
no code implementations • 29 Mar 2021 • Guotong Xue, Ming Zhong, JianXin Li, Jia Chen, Chengshuai Zhai, Ruochen Kong
Due to the lack of comprehensive investigation of them, we give a survey of dynamic network embedding in this paper.
no code implementations • 30 Jan 2021 • Mauro Maggioni, Jason Miller, Hongda Qiu, Ming Zhong
Interacting agent and particle systems are extensively used to model complex phenomena in science and engineering.
2 code implementations • Findings of the Association for Computational Linguistics 2020 • Yiran Chen, PengFei Liu, Ming Zhong, Zi-Yi Dou, Danqing Wang, Xipeng Qiu, Xuanjing Huang
In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora.
no code implementations • 8 Oct 2020 • Jason Miller, Sui Tang, Ming Zhong, Mauro Maggioni
Modeling the complex interactions of systems of particles or agents is a fundamental scientific and mathematical problem that is studied in diverse fields, ranging from physics and biology, to economics and machine learning.
2 code implementations • ACL 2020 • Ming Zhong, PengFei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, Xuanjing Huang
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Ranked #1 on
Text Summarization
on BBC XSum
1 code implementation • 23 Dec 2019 • Mauro Maggioni, Jason Miller, Ming Zhong
We consider the fundamental problem of inferring interaction kernels from observations of agent-based dynamical systems given observations of trajectories, in particular for collective dynamical systems exhibiting emergent behaviors with complicated interaction kernels, in a nonparametric fashion, and for kernels which are parametrized by a single unknown parameter.
no code implementations • WS 2019 • Ming Zhong, Danqing Wang, PengFei Liu, Xipeng Qiu, Xuanjing Huang
In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models.
no code implementations • 28 Sep 2019 • Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter C. Ma, Qiumin Xu, Ming Zhong, Hanxiao Liu, Anna Goldie, Azalia Mirhoseini, James Laudon
Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices.
no code implementations • 30 Aug 2019 • Danqing Wang, PengFei Liu, Ming Zhong, Jie Fu, Xipeng Qiu, Xuanjing Huang
Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization.
2 code implementations • ACL 2019 • Ming Zhong, PengFei Liu, Danqing Wang, Xipeng Qiu, Xuanjing Huang
The recent years have seen remarkable success in the use of deep neural networks on text summarization.
Ranked #6 on
Extractive Text Summarization
on CNN / Daily Mail
1 code implementation • 14 Dec 2018 • Fei Lu, Mauro Maggioni, Sui Tang, Ming Zhong
Inferring the laws of interaction between particles and agents in complex dynamical systems from observational data is a fundamental challenge in a wide variety of disciplines.
no code implementations • 1 Feb 2015 • Zhijun Chen, Chaozhong Wu, Yishi Zhang, Zhen Huang, Bin Ran, Ming Zhong, Nengchao Lyu
Feature selection has attracted significant attention in data mining and machine learning in the past decades.