Search Results for author: Yixin Liu

Found 41 papers, 26 papers with code

Learning Strong Graph Neural Networks with Weak Information

1 code implementation29 May 2023 Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan

Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.

Graph Learning

BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

1 code implementation26 May 2023 Kai Zhang, Jun Yu, Zhiling Yan, Yixin Liu, Eashan Adhikarla, Sunyang Fu, Xun Chen, Chen Chen, Yuyin Zhou, Xiang Li, Lifang He, Brian D. Davison, Quanzheng Li, Yong Chen, Hongfang Liu, Lichao Sun

In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks.

On Learning to Summarize with Large Language Models as References

1 code implementation23 May 2023 Yixin Liu, Alexander R. Fabbri, PengFei Liu, Dragomir Radev, Arman Cohan

Therefore, we investigate a new learning paradigm of text summarization models that considers the LLMs as the reference or the gold-standard oracle on commonly used summarization datasets such as the CNN/DailyMail dataset.

Contrastive Learning Text Summarization

A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT

1 code implementation7 Mar 2023 Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, Lichao Sun

The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.

Securing Biomedical Images from Unauthorized Training with Anti-Learning Perturbation

no code implementations5 Mar 2023 Yixin Liu, Haohui Ye, Kai Zhang, Lichao Sun

The volume of open-source biomedical data has been essential to the development of various spheres of the healthcare community since more `free' data can provide individual researchers more chances to contribute.

Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation

no code implementations5 Mar 2023 Yixin Liu, Chenrui Fan, Pan Zhou, Lichao Sun

While the use of graph-structured data in various fields is becoming increasingly popular, it also raises concerns about the potential unauthorized exploitation of personal data for training commercial graph neural network (GNN) models, which can compromise privacy.

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling +1

On Improving Summarization Factual Consistency from Natural Language Feedback

1 code implementation20 Dec 2022 Yixin Liu, Budhaditya Deb, Milagro Teruel, Aaron Halfaker, Dragomir Radev, Ahmed H. Awadallah

In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment.

Text Generation

Needle in a Haystack: An Analysis of Finding Qualified Workers on MTurk for Summarization

no code implementations20 Dec 2022 Lining Zhang, João Sedoc, Simon Mille, Yufang Hou, Sebastian Gehrmann, Daniel Deutsch, Elizabeth Clark, Yixin Liu, Miruna Clinciu, Saad Mahamood, Khyathi Chandu

The acquisition of high-quality human annotations through crowdsourcing platforms like Amazon Mechanical Turk (MTurk) is more challenging than expected.

Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation

2 code implementations15 Dec 2022 Yixin Liu, Alexander R. Fabbri, PengFei Liu, Yilun Zhao, Linyong Nan, Ruilin Han, Simeng Han, Shafiq Joty, Chien-Sheng Wu, Caiming Xiong, Dragomir Radev

4) We evaluate existing automatic metrics using the collected human annotations across evaluation protocols and demonstrate how our benchmark leads to more statistically stable and significant results.

Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

1 code implementation25 Nov 2022 Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee, Shirui Pan

Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges.

Graph Representation Learning

SEAT: Stable and Explainable Attention

no code implementations23 Nov 2022 Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang

Results show that SEAT is more stable against different perturbations and randomness while also keeps the explainability of attention, which indicates it is a more faithful explanation.

Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

1 code implementation23 Nov 2022 Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang

Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.

Federated Learning Graph Learning

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

1 code implementation8 Nov 2022 Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan

As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.

Contrastive Learning Data Augmentation +2

GEMv2: Multilingual NLG Benchmarking in a Single Line of Code

no code implementations22 Jun 2022 Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina McMillan-Major, Anna Shvets, Ashish Upadhyay, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez-Beltrachini, Leonardo F. R. Ribeiro, Lewis Tunstall, Li Zhang, Mahima Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou

This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.

Benchmarking Text Generation

Leveraging Locality in Abstractive Text Summarization

1 code implementation25 May 2022 Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev

Our experimental results show that our model has a better performance compared with strong baselines with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.

Abstractive Text Summarization Text Generation

BRIO: Bringing Order to Abstractive Summarization

2 code implementations ACL 2022 Yixin Liu, PengFei Liu, Dragomir Radev, Graham Neubig

Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to the reference summary.

Abstractive Text Summarization

DataLab: A Platform for Data Analysis and Intervention

no code implementations ACL 2022 Yang Xiao, Jinlan Fu, Weizhe Yuan, Vijay Viswanathan, Zhoumianze Liu, Yixin Liu, Graham Neubig, PengFei Liu

Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.

Graph Neural Networks for Graphs with Heterophily: A Survey

no code implementations14 Feb 2022 Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, Philip S. Yu

Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriads of graph analytic tasks and applications.

From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach

no code implementations11 Feb 2022 Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen

Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.

Anomaly Detection Contrastive Learning

Towards Unsupervised Deep Graph Structure Learning

1 code implementation17 Jan 2022 Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan

To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.

Contrastive Learning Graph structure learning

Surfer100: Generating Surveys From Web Resources, Wikipedia-style

no code implementations LREC 2022 Irene Li, Alexander Fabbri, Rina Kawamura, Yixin Liu, Xiangru Tang, Jaesung Tae, Chang Shen, Sally Ma, Tomoe Mizutani, Dragomir Radev

Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely.

Language Modelling

Distribution Locational Marginal Pricing Under Uncertainty Considering Coordination of Distribution and Wholesale Markets

no code implementations14 Oct 2021 Zongzheng Zhao, Yixin Liu, Li Guo, Linquan Bai, Chengshan Wang

An effective distribution electricity market (DEM) is required to manage the rapidly growing small-scale distributed energy resources (DERs) in distribution systems (DSs).


Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

1 code implementation23 Aug 2021 Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen

While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.

Anomaly Detection Contrastive Learning +2

Anomaly Detection in Dynamic Graphs via Transformer

1 code implementation18 Jun 2021 Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent CS Lee

Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.

Anomaly Detection

SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

2 code implementations ACL 2021 Yixin Liu, PengFei Liu

In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i. e., quality estimation) assisted by contrastive learning.

Abstractive Text Summarization Contrastive Learning +1

RefSum: Refactoring Neural Summarization

1 code implementation NAACL 2021 Yixin Liu, Zi-Yi Dou, PengFei Liu

Although some recent works show potential complementarity among different state-of-the-art systems, few works try to investigate this problem in text summarization.

Text Summarization

ExplainaBoard: An Explainable Leaderboard for NLP

1 code implementation ACL 2021 PengFei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaicheng Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, Zi-Yi Dou, Graham Neubig

In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e. g.~what is the best-performing system bad at?)

Machine Translation

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

1 code implementation27 Feb 2021 Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis

Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.

Anomaly Detection Contrastive Learning +1

Cyclic Label Propagation for Graph Semi-supervised Learning

no code implementations24 Nov 2020 Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, Jiajun Bu

To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.

Node Classification

On Learning Text Style Transfer with Direct Rewards

1 code implementation NAACL 2021 Yixin Liu, Graham Neubig, John Wieting

In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task.

Machine Translation Semantic Similarity +4

Conditional Automated Channel Pruning for Deep Neural Networks

no code implementations21 Sep 2020 Yixin Liu, Yong Guo, Zichang Liu, Haohua Liu, Jingjie Zhang, Zejun Chen, Jing Liu, Jian Chen

To address this issue, given a target compression rate for the whole model, one can search for the optimal compression rate for each layer.

Model Compression

Training and Inference Methods for High-Coverage Neural Machine Translation

no code implementations WS 2020 Michael Yang, Yixin Liu, Rahul Mayuranath

In this paper, we introduce a system built for the Duolingo Simultaneous Translation And Paraphrase for Language Education (STAPLE) shared task at the 4th Workshop on Neural Generation and Translation (WNGT 2020).

Machine Translation Translation +1

Adversarial Attack and Defense on Graph Data: A Survey

1 code implementation26 Dec 2018 Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Yixin Liu, Philip S. Yu, Lifang He, Bo Li

Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.

Adversarial Attack Image Classification +1

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