28 citations · 29 across the 13 of their papers we have counts for
13 papers
Decomposing the Basic Abilities of Large Language Models: Mitigating Cross-Task Interference in Multi-Task Instruct-Tuning
Bing Wang, Ximing Li, Changchun Li +3
Recently, the prominent performance of large language models (LLMs) has been largely driven by multi-task instruct-tuning. Unfortunately, this training paradigm suffers from a key…
Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios
Bing Wang, Ximing Li, Changchun Li +4
Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automa…
Enhancing Multimodal Misinformation Detection by Replaying the Whole Story from Image Modality Perspective
Bing Wang, Ximing Li, Yanjun Wang +4
Multimodal Misinformation Detection (MMD) refers to the task of detecting social media posts involving misinformation, where the post often contains text and image modalities. Howe…
Variety Is the Spice of Life: Detecting Misinformation with Dynamic Environmental Representations
Bing Wang, Ximing Li, Yiming Wang +4
The proliferation of misinformation across diverse social media platforms has drawn significant attention from both academic and industrial communities due to its detrimental effec…
Remember Past, Anticipate Future: Learning Continual Multimodal Misinformation Detectors
Bing Wang, Ximing Li, Mengzhe Ye +4
Nowadays, misinformation articles, especially multimodal ones, are widely spread on social media platforms and cause serious negative effects. To control their propagation, Multimo…
Robust Misinformation Detection by Visiting Potential Commonsense Conflict
Bing Wang, Ximing Li, Changchun Li +4
The development of Internet technology has led to an increased prevalence of misinformation, causing severe negative effects across diverse domains. To mitigate this challenge, Mis…
Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
Bing Wang, Bingrui Zhao, Ximing Li +3
Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor…
Why Misinformation is Created? Detecting them by Integrating Intent Features
Bing Wang, Ximing Li, Changchun Li +3
Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full…
Harmfully Manipulated Images Matter in Multimodal Misinformation Detection
Bing Wang, Shengsheng Wang, Changchun Li +2
Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying m…
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations
Jihong Ouyang, Zhiyao Yang, Silong Liang +3
Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has received much attention recently. Many works investigate sentiment information through opi…
Unsupervised Sentence Representation Learning with Frequency-induced Adversarial Tuning and Incomplete Sentence Filtering
Bing Wang, Ximing Li, Zhiyao Yang +3
Pre-trained Language Model (PLM) is nowadays the mainstay of Unsupervised Sentence Representation Learning (USRL). However, PLMs are sensitive to the frequency information of words…
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis
Bing Wang, Liang Ding, Qihuang Zhong +2
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence. However, it is…
Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals
Bing Wang, Yue Wang, Ximing Li +1
Dataless text classification, i.e., a new paradigm of weakly supervised learning, refers to the task of learning with unlabeled documents and a few predefined representative words…