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Beibei Li

8 accepted papers

2026

Look Closer! An Adversarial Parametric Editing Framework for Hallucination Mitigation in VLMs

AAAI 2026technical

While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they exhibit persistent hallucination issues, generating outputs misaligned with visual inputs. Recent studies attribute these hallucinations to VLMs

Cited by 0SourcePDFScholar
2025

Fairshare Data Pricing via Data Valuation for Large Language Models

NeurIPS 2025poster

Training data is the backbone of large language models (LLMs), yet today’s data markets often operate under exploitative pricing -- sourcing data from marginalized groups with little pay or recognition. This paper introduces a theoretical framework for LLM data markets, modeling the strategic intera…

Cited by 0SourceScholar
2025

FracFace: Breaking The Visual Clues—Fractal-Based Privacy-Preserving Face Recognition

NeurIPS 2025poster

Face recognition is essential for identity authentication, but the rich visual clues in facial images pose significant privacy risks, highlighting the critical importance of privacy-preserving solutions. For instance, numerous studies have shown that generative models are capable of effectively perf…

Cited by 0SourceScholar
2025

Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience

AAAI 2025technical

End-to-end training with global optimization have popularized graph neural networks (GNNs) for node classification, yet inadvertently introduced vulnerabilities to adversarial edge-perturbing attacks. Adversaries can exploit the inherent opened interfaces of GNNs' input and output, perturbing critic…

Cited by 0SourcePDFScholar
2025

Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending Against Poisoning Attacks

AAAI 2025technical

Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial poisoning attacks on node classification tasks. Current defensive methods require substituting the original GNNs with defense models, regardless of the original's type. This approach, while targeting advers…

Cited by 0SourcePDFScholar
2025

Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network

AAAI 2025technical

Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous respectable TSG methods have achieved remarkable success, they train each video-query pair separately and ignore the re…

Cited by 4SourcePDFScholar
2024

Towards Inductive Robustness: Distilling and Fostering Wave-Induced Resonance in Transductive GCNs against Graph Adversarial Attacks

AAAI 2024technical

Graph neural networks (GNNs) have recently been shown to be vulnerable to adversarial attacks, where slight perturbations in the graph structure can lead to erroneous predictions. However, current robust models for defending against such attacks inherit the transductive limitations of graph convolut…

Cited by 5SourcePDFScholar
2022

A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance

IJCAI 2022poster

In this paper, we analyze the responses of three major US cable news networks to three seminal policing events in the US spanning a thirteen month period--the murder of George Floyd by police officer Derek Chauvin, the Capitol riot, Chauvin's conviction, and his sentencing. We cast the problem of ag…