7 citations · 11 across the 19 of their papers we have counts for
20 papers
Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability
Lingwei Wei, Dou Hu, Wei Zhou +2
Large language models (LLMs) have transformed misinformation from a primarily content-centric problem into a broader ecosystem-level security challenge. When misused, LLMs create r…
InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition
Fengze Liu, Weidong Zhou, Binbin Liu +7
Upweighting high-quality data in LLM pretraining often improves performance, but in datalimited regimes, especially under overtraining, stronger upweighting increases repetition an…
An Information-theoretic Propagation Denoising and Fusion Framework for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou +1
Incomplete propagation data significantly hinders robust fake news detection. Recent approaches leverage large language models to simulate missing user interactions via role-playin…
Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection
Mengyang Chen, Lingwei Wei, Han Cao +3
Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news detection methods primarily…
Diagnosing Retrieval Bias Under Multiple In-Context Knowledge Updates in Large Language Models
Boyu Qiao, Sean Guo, Xian Yang +4
LLMs are widely used in knowledge-intensive tasks where the same fact may be revised multiple times within context. Unlike prior work focusing on one-shot updates or single conflic…
Agent Tools Orchestration Leaks More: Dataset, Benchmark, and Mitigation
Yuxuan Qiao, Dongqin Liu, Hongchang Yang +2
LLM agents can combine individually non-revealing tool returns and disclose a sensitive conclusion, creating Tools Orchestration Privacy Risk (TOP-R). We formalize TOP-R through th…
Structure-aware Propagation Generation with Large Language Models for Fake News Detection
Mengyang Chen, Lingwei Wei, Wei Zhou +1
The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how i…
MPPFND: A Dataset and Analysis of Detecting Fake News with Multi-Platform Propagation
Congyuan Zhao, Lingwei Wei, Ziming Qin +3
Fake news spreads widely on social media, leading to numerous negative effects. Most existing detection algorithms focus on analyzing news content and social context to detect fake…
Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models
Han Cao, Lingwei Wei, Wei Zhou +1
The rapid development of social platforms exacerbates the dissemination of misinformation, which stimulates the research in fact verification. Recent studies tend to leverage seman…
An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding
Dou Hu, Lingwei Wei, Wei Zhou +1
This paper proposes a new principled multi-task representation learning framework (InfoMTL) to extract noise-invariant sufficient representations for all tasks. It ensures sufficie…
DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs
Minxuan Lv, Zhenpeng Su, Leiyu Pan +10
As large language models continue to scale, computational costs and resource consumption have emerged as significant challenges. While existing sparsification methods like pruning…
MaskMoE: Boosting Token-Level Learning via Routing Mask in Mixture-of-Experts
Zhenpeng Su, Zijia Lin, Xue Bai +8
Scaling the size of a model enhances its capabilities but significantly increases computation complexity. Mixture-of-Experts models (MoE) address the issue by allowing model size t…
Multi-source Knowledge Enhanced Graph Attention Networks for Multimodal Fact Verification
Han Cao, Lingwei Wei, Wei Zhou +1
Multimodal fact verification is an under-explored and emerging field that has gained increasing attention in recent years. The goal is to assess the veracity of claims that involve…
Transferring Structure Knowledge: A New Task to Fake news Detection Towards Cold-Start Propagation
Lingwei Wei, Dou Hu, Wei Zhou +1
Many fake news detection studies have achieved promising performance by extracting effective semantic and structure features from both content and propagation trees. However, it is…
Representation Learning with Conditional Information Flow Maximization
Dou Hu, Lingwei Wei, Wei Zhou +1
This paper proposes an information-theoretic representation learning framework, named conditional information flow maximization, to extract noise-invariant sufficient representatio…
Structured Probabilistic Coding
Dou Hu, Lingwei Wei, Yaxin Liu +2
This paper presents a new supervised representation learning framework, namely structured probabilistic coding (SPC), to learn compact and informative representations from input re…
Are Large Language Models Good Fact Checkers: A Preliminary Study
Han Cao, Lingwei Wei, Mengyang Chen +2
Recently, Large Language Models (LLMs) have drawn significant attention due to their outstanding reasoning capabilities and extensive knowledge repository, positioning them as supe…
Explore the Potential of LLMs in Misinformation Detection: An Empirical Study
Mengyang Chen, Lingwei Wei, Han Cao +2
Large Language Models (LLMs) have garnered significant attention for their powerful ability in natural language understanding and reasoning. In this paper, we present a comprehensi…
MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models
Zhenpeng Su, Xing Wu, Xue Bai +5
Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonst…
HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus
Zhenpeng Su, Xing Wu, Wei Zhou +2
ChatGPT has garnered significant interest due to its impressive performance; however, there is growing concern about its potential risks, particularly in the detection of AI-genera…