2 citations · 7 across the 15 of their papers we have counts for
17 papers
Beyond Final Outcomes: Quantifying Claim-Level Information Dynamics in Multi-Agent LLM Interaction
Haocheng Jiang, Herun Wan, Yongyuan Peng +1
When LLM-powered agents interact, information is repeatedly shared, expanded, and forgotten, yet multi-agent evaluation typically focuses on final task outcomes. We study such inte…
Why Knowing Both Hops Is Not Enough: Understanding Two-Hop Generalization in Language Models
Zili Zhang, Yilin Wang, Heng Wang +2
Large language models (LLMs) can solve complex multi-hop problems yet exhibit puzzling failures on simple two-hop queries: although a model may correctly store each individual hop,…
Listen, See and Track: Spatio-Temporal Audio-Visual Sound Event Reasoning for Omni-Modal Language Models
Zhi Zeng, Cheng Zhang, Zesheng Yang +9
Understanding dynamic sound sources requires jointly determining what produces a sound, where the source is located, and how it moves over time. Yet existing audio-language models…
The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation
Herun Wan, Jiaying Wu, Minnan Luo +4
Multi-agent LLM systems often treat consensus as evidence of successful interaction. For deliberative problems, however, reliability depends on whether agents preserve the facts an…
From Manipulation to Mistrust: Explaining Diverse Micro-Video Misinformation for Robust Debunking in the Wild
Zhi Zeng, Yifei Yang, Jiaying Wu +5
The rise of micro-videos has reshaped how misinformation spreads, amplifying its speed, reach, and impact on public trust. Existing benchmarks typically focus on a single deception…
The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence
Herun Wan, Jiaying Wu, Minnan Luo +3
To reliably assist human decision-making, LLMs must maintain factual internal beliefs against misleading injections. While current models resist explicit misinformation, we uncover…
Bot Meets Shortcut: How Can LLMs Aid in Handling Unknown Invariance OOD Scenarios?
Shiyan Zheng, Herun Wan, Minnan Luo +1
While existing social bot detectors perform well on benchmarks, their robustness across diverse real-world scenarios remains limited due to unclear ground truth and varied misleadi…
GuessBench: Sensemaking Multimodal Creativity in the Wild
Zifeng Zhu, Shangbin Feng, Herun Wan +3
We propose GuessBench, a novel benchmark that evaluates Vision Language Models (VLMs) on modeling the pervasive, noisy, and pluralistic human creativity. GuessBench sources data fr…
HACo-Det: A Study Towards Fine-Grained Machine-Generated Text Detection under Human-AI Coauthoring
Zhixiong Su, Yichen Wang, Herun Wan +2
The misuse of large language models (LLMs) poses potential risks, motivating the development of machine-generated text (MGT) detection. Existing literature primarily concentrates o…
Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection
Herun Wan, Jiaying Wu, Minnan Luo +2
Misinformation detection models often rely on superficial cues (i.e., \emph{shortcuts}) that correlate with misinformation in training data but fail to generalize to the diverse an…
Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
Hao Guo, Zihan Ma, Zhi Zeng +4
Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake n…
On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs
Herun Wan, Minnan Luo, Zhixiong Su +2
Evidence-enhanced detectors present remarkable abilities in identifying malicious social text. However, the rise of large language models (LLMs) brings potential risks of evidence…
How Do Social Bots Participate in Misinformation Spread? A Comprehensive Dataset and Analysis
Herun Wan, Minnan Luo, Zihan Ma +2
Social media platforms provide an ideal environment to spread misinformation, where social bots can accelerate the spread. This paper explores the interplay between social bots and…
DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection
Herun Wan, Shangbin Feng, Zhaoxuan Tan +3
Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accu…
What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
Shangbin Feng, Herun Wan, Ningnan Wang +3
Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we b…
GADY: Unsupervised Anomaly Detection on Dynamic Graphs
Shiqi Lou, Qingyue Zhang, Shujie Yang +3
Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field…
BotPercent: Estimating Bot Populations in Twitter Communities
Zhaoxuan Tan, Shangbin Feng, Melanie Sclar +4
Twitter bot detection is vital in combating misinformation and safeguarding the integrity of social media discourse. While malicious bots are becoming more and more sophisticated a…