16 citations · 40 across the 23 of their papers we have counts for
23 papers
World Model-Guided Reinforcement Learning via Counterfactual User Engagement Simulation
Ang Li, Xin Xu, Bin Liang +4
Reinforcement learning for user-centric agents is limited by the cost, latency, and risk of collecting online feedback, as well as by the lack of counterfactual comparisons under t…
Social inequality and cultural factors impact the awareness and reaction during the cryptic transmission period of pandemic
Zhuoren Jiang, Xiaozhong Liu, Yangyang Kang +3
The World Health Organization (WHO) declared the COVID-19 outbreak a Public Health Emergency of International Concern (PHEIC) on January 31, 2020. However, rumors of a "mysterious…
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model
Tianqianjin Lin, Pengwei Yan, Kaisong Song +7
Graph foundation models (GFMs) have recently gained significant attention. However, the unique data processing and evaluation setups employed by different studies hinder a deeper u…
A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis
Kaisong Song, Yangyang Kang, Jiawei Liu +3
User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is…
Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration
Weikang Yuan, Junjie Cao, Zhuoren Jiang +7
Large Language Models (LLMs) could struggle to fully understand legal theories and perform complex legal reasoning tasks. In this study, we introduce a challenging task (confusing…
Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs
Chengyuan Liu, Shihang Wang, Lizhi Qing +4
While Large Language Models (LLMs) demonstrate impressive generation abilities, they frequently struggle when it comes to specialized domains due to their limited domain-specific k…
RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU
Chengyuan Liu, Shihang Wang, Fubang Zhao +5
Information Extraction (IE) and Text Classification (CLS) serve as the fundamental pillars of NLU, with both disciplines relying on analyzing input sequences to categorize outputs…
More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs
Chengyuan Liu, Yangyang Kang, Shihang Wang +5
The performance on general tasks decreases after Large Language Models (LLMs) are fine-tuned on domain-specific tasks, the phenomenon is known as Catastrophic Forgetting (CF). Howe…
Enhance Robustness of Language Models Against Variation Attack through Graph Integration
Zi Xiong, Lizhi Qing, Yangyang Kang +5
The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adv…
Personalized LLM Response Generation with Parameterized Memory Injection
Kai Zhang, Yejin Kim, Xiaozhong Liu
Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, personalized LLM response generation holds t…
Evolving Knowledge Distillation with Large Language Models and Active Learning
Chengyuan Liu, Yangyang Kang, Fubang Zhao +4
Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, p…
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery
Pengwei Yan, Kaisong Song, Zhuoren Jiang +4
While self-supervised graph pretraining techniques have shown promising results in various domains, their application still experiences challenges of limited topology learning, hum…
Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph
Tianqianjin Lin, Kaisong Song, Zhuoren Jiang +6
Heterogeneous graph neural networks have become popular in various domains. However, their generalizability and interpretability are limited due to the discrepancy between their in…
Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction
Yang Wu, Xurui Li, Xuhong Zhang +3
Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data…
Goal-Oriented Prompt Attack and Safety Evaluation for LLMs
Chengyuan Liu, Fubang Zhao, Lizhi Qing +4
Large Language Models (LLMs) presents significant priority in text understanding and generation. However, LLMs suffer from the risk of generating harmful contents especially while…
LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination
Kai Zhang, Yangyang Kang, Fubang Zhao +1
Large Language Models (LLMs), such as GPT3.5, have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, medical assistants hold the…
PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts
Kaiwen Wei, Jie Yao, Jingyuan Zhang +6
Key Information Extraction (KIE) is a challenging multimodal task that aims to extract structured value semantic entities from visually rich documents. Although significant progres…
H2CGL: Modeling Dynamics of Citation Network for Impact Prediction
Guoxiu He, Zhikai Xue, Zhuoren Jiang +3
The potential impact of a paper is often quantified by how many citations it will receive. However, most commonly used models may underestimate the influence of newly published pap…
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction
Chengyuan Liu, Fubang Zhao, Yangyang Kang +5
Universal Information Extraction (UIE) is an area of interest due to the challenges posed by varying targets, heterogeneous structures, and demand-specific schemas. However, previo…
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models
Jiawei Liu, Yangyang Kang, Di Tang +5
Neural text ranking models have witnessed significant advancement and are increasingly being deployed in practice. Unfortunately, they also inherit adversarial vulnerabilities of g…
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning
Jiannan Guo, Yangyang Kang, Yu Duan +6
While annotating decent amounts of data to satisfy sophisticated learning models can be cost-prohibitive for many real-world applications. Active learning (AL) and semi-supervised…
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis
Jiawei Liu, Kaisong Song, Yangyang Kang +5
Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital appreh…
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation
Zhuoren Jiang, Zhe Gao, Guoxiu He +5
The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model…