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Ping Kuang

7 accepted papers

2026

Causally-Grounded Dual-Path Attention Intervention for Object Hallucination Mitigation in LVLMs

AAAI 2026technical

Object hallucination remains a critical challenge in Large Vision-Language Models (LVLMs), where models generate content inconsistent with visual inputs. Existing language-decoder based mitigation approaches often regulate visual or textual attention independently, overlooking their interaction as t

Cited by 0SourcePDFScholar
2026

Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding

ICML 2026poster

Large Vision-Language Models (LVLMs) exhibit sophisticated reasoning but remain susceptible to object hallucination. Deviating from the prevailing attention intensity assumption, we reveal a deeper dynamic structural misalignment: hallucination is triggered at decision-critical steps where specific …

Cited by 0SourceScholar
2025

Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences

ICASSP 2025accepted

Pre-trained language models (PLMs) are trained on data that inherently contains gender biases, leading to undesirable impacts. Traditional debiasing methods often rely on external corpora, which may lack quality, diversity, or demographic balance, affecting the effectiveness of debiasing. With the r…

Cited by 0SourceScholar
2024

Amplifying Diversity and Quality in Commonsense Knowledge Graph Completion (Student Abstract)

AAAI 2024technical

Conventional commonsense knowledge graph completion (CKGC) methods provide inadequate sequence when fine-tuning or generating stages and incorporate full fine-tuning, which fail to align with the autoregressive model's pre-training patterns and have insufficient parameter efficiency. Moreover, decod…

Cited by 2SourcePDFScholar
2024

Biases Mitigation and Expressiveness Preservation in Language Models: A Comprehensive Pipeline (Student Abstract)

AAAI 2024technical

Pre-trained language models (PLMs) have greatly transformed various downstream tasks, yet frequently display social biases from training data, raising fairness concerns. Recent efforts to debias PLMs come with limitations: they either fine-tune the entire parameters in PLMs, which is time-consuming…

Cited by 3SourcePDFScholar
2024

Disentanglement-Guided Spatial-Temporal Graph Neural Network for Metro Flow Forecasting (Student Abstract)

AAAI 2024technical

In recent intelligent transportation applications, metro flow forecasting has received much attention from researchers. Most prior arts endeavor to explore spatial or temporal dependencies while ignoring the key characteristic patterns underlying historical flows, e.g., trend and periodicity. Althou…

Cited by 0SourcePDFScholar
2023

Mobility Prediction via Sequential Trajectory Disentanglement (Student Abstract)

AAAI 2023technical

Accurately predicting human mobility is a critical task in location-based recommendation. Most prior approaches focus on fusing multiple semantics trajectories to forecast the future movement of people, and fail to consider the distinct relations in underlying context of human mobility, resulting in…

Cited by 1SourcePDFScholar