27 citations · 34 across the 15 of their papers we have counts for
15 papers
Fine-grained Verification via Diagnostic Reasoning Supervision for Aspect Sentiment Triplet Extraction
Wenna Lai, Haoran Xie, Guandong Xu +2
Aspect Sentiment Triplet Extraction (ASTE) aims to identify aspect terms, opinion terms, and sentiment polarities as structured triplets, providing essential inputs for downstream…
Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Xiangmeng Wang, Qian Li, Haiyang Xia +3
Heterophily is a prevalent property of real-world graphs and is well known to impair the performance of homophilic Graph Neural Networks (GNNs). Prior work has attempted to adapt G…
Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
Sirui Huang, Jing Long, Qian Li +2
Sequential Recommendation (SR) predicts users next interactions by modeling the temporal order of their historical behaviors. Existing approaches, including traditional sequential…
Listwise Preference Optimization with Element-wise Confusions for Aspect Sentiment Quad Prediction
Wenna Lai, Haoran Xie, Guandong Xu +2
Aspect sentiment quad prediction (ASQP) is inherently challenging to predict a structured quadruple with four core sentiment elements, including aspect term (a), aspect category (c…
When LLMs Team Up: The Emergence of Collaborative Affective Computing
Wenna Lai, Haoran Xie, Guandong Xu +2
Affective Computing (AC) is essential in bridging the gap between human emotional experiences and machine understanding. Traditionally, AC tasks in natural language processing (NLP…
HyperG: Hypergraph-Enhanced LLMs for Structured Knowledge
Sirui Huang, Hanqian Li, Yanggan Gu +3
Given that substantial amounts of domain-specific knowledge are stored in structured formats, such as web data organized through HTML, Large Language Models (LLMs) are expected to…
STAR: Stepwise Task Augmentation with Relation Learning for Aspect Sentiment Quad Prediction
Wenna Lai, Haoran Xie, Guandong Xu +1
Aspect-based sentiment analysis (ABSA) aims to identify four sentiment elements, including aspect term, aspect category, opinion term, and sentiment polarity. These elements constr…
Multi-Task Learning with LLMs for Implicit Sentiment Analysis: Data-level and Task-level Automatic Weight Learning
Wenna Lai, Haoran Xie, Guandong Xu +1
Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words. Previous methods have struggled with insufficient data and limited reason…
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Sirui Huang +2
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learnin…
Reasoning Factual Knowledge in Structured Data with Large Language Models
Sirui Huang, Yanggan Gu, Xuming Hu +3
Large language models (LLMs) have made remarkable progress in various natural language processing tasks as a benefit of their capability to comprehend and reason with factual knowl…
RVISA: Reasoning and Verification for Implicit Sentiment Analysis
Wenna Lai, Haoran Xie, Guandong Xu +1
With an increasing social demand for fine-grained sentiment analysis (SA), implicit sentiment analysis (ISA) poses a significant challenge with the absence of salient cue words in…
Expert-Guided Extinction of Toxic Tokens for Debiased Generation
Xueyao Sun, Kaize Shi, Haoran Tang +2
Large language models (LLMs) can elicit social bias during generations, especially when inference with toxic prompts. Controlling the sensitive attributes in generation encounters…
Compressing Long Context for Enhancing RAG with AMR-based Concept Distillation
Kaize Shi, Xueyao Sun, Qing Li +1
Large Language Models (LLMs) have made significant strides in information acquisition. However, their overreliance on potentially flawed parametric knowledge leads to hallucination…
Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
Zhaoyang Chu, Yao Wan, Qian Li +5
Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding…
Neural Causal Graph Collaborative Filtering
Xiangmeng Wang, Qian Li, Dianer Yu +2
Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One…