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Found 69 papers, 37 papers with code
Authorship Attribution for Neural Text Generation
In recent years, the task of generating realistic short and long texts have made tremendous advancements.
Can Multimodal LLMs Perform Time Series Anomaly Detection?
Starting with the univariate case (point- and range-wise anomalies), we extend our evaluation to more practical scenarios, including multivariate and irregular time series scenarios, and variate-wise anomalies.
Benchmarking LLMs for Political Science: A United Nations Perspective
Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplored.
Understanding and Tackling Label Errors in Individual-Level Nature Language Understanding
Some tasks, such as stance detection and sentiment analysis, are closely related to individual subjective perspectives, thus termed individual-level NLU.
Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection
To tackle these issues, we propose BREAK, a broad-range semantics model for fake news detection that leverages a fully connected graph to capture comprehensive semantics while employing dual denoising modules to minimize both structural and feature noise.
ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges
To validate the proposed techniques, we introduce ConQRet, a new benchmark featuring long and complex human-authored arguments on debated topics, grounded in real-world websites, allowing an exhaustive evaluation across retrieval effectiveness, argument quality, and groundedness.
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP).
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams.
Can Knowledge Editing Really Correct Hallucinations?
Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual information in generated content, despite their superior capacities across tasks.
Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach
Specifically, the proposed framework EnergyUCB (1) balances the performance-energy trade-off in the reward function, (2) effectively navigates the exploration & exploitation dilemma when adjusting GPU core frequencies online, and (3) leverages the ratio of GPU core utilization to uncore utilization as a real-time GPU performance metric.
FODA-PG for Enhanced Medical Imaging Narrative Generation: Adaptive Differentiation of Normal and Abnormal Attributes
Automatic Medical Imaging Narrative generation aims to alleviate the workload of radiologists by producing accurate clinical descriptions directly from radiological images.
Model Attribution in LLM-Generated Disinformation: A Domain Generalization Approach with Supervised Contrastive Learning
To address this, we introduce a novel approach based on Supervised Contrastive Learning.
Can Editing LLMs Inject Harm?
Then, we find that editing attacks can inject both types of misinformation into LLMs, and the effectiveness is particularly high for commonsense misinformation injection.
Taxonomy-Guided Zero-Shot Recommendations with LLMs
With the emergence of large language models (LLMs) and their ability to perform a variety of tasks, their application in recommender systems (RecSys) has shown promise.
LMO-DP: Optimizing the Randomization Mechanism for Differentially Private Fine-Tuning (Large) Language Models
To address such limitations, we propose a novel Language Model-based Optimal Differential Privacy (LMO-DP) mechanism, which takes the first step to enable the tight composition of accurately fine-tuning (large) language models with a sub-optimal DP mechanism, even in strong privacy regimes (e. g., $0. 1\leq \epsilon<3$).
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Long-Short Range Time Series Forecasting
To meet the objectives, we propose a multi-scale hybrid Mamba-Transformer experts model State Space Transformer (SST).
Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large.
Can Large Language Models Identify Authorship?
(3) Can LLMs provide explainability in authorship analysis, particularly through the role of linguistic features?
Can Large Language Model Agents Simulate Human Trust Behavior?
In this paper, we focus on one critical and elemental behavior in human interactions, trust, and investigate whether LLM agents can simulate human trust behavior.
Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization
Nevertheless, for the dialogue summarization task, which aims to generate summaries for different roles in dialogue, most of the state-of-the-art methods conduct on small models (e. g Bart and Bert).
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection
Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when the data is scarce or imbalanced.
Trojan Activation Attack: Red-Teaming Large Language Models using Activation Steering for Safety-Alignment
To ensure AI safety, instruction-tuned Large Language Models (LLMs) are specifically trained to ensure alignment, which refers to making models behave in accordance with human intentions.
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models
This work investigates the potential of undermining both fairness and detection performance in abusive language detection.
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models
While existing works on claim verification have shown promising results, a crucial piece of the puzzle that remains unsolved is to understand how to verify claims without relying on human-annotated data, which is expensive to create at a large scale.
Can LLM-Generated Misinformation Be Detected?
Then, through extensive empirical investigation, we discover that LLM-generated misinformation can be harder to detect for humans and detectors compared to human-written misinformation with the same semantics, which suggests it can have more deceptive styles and potentially cause more harm.
Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation
Fact-checking in financial domain is under explored, and there is a shortage of quality dataset in this domain.
Investigating Online Financial Misinformation and Its Consequences: A Computational Perspective
In conclusion, this research paper sheds light on the pervasive issue of online financial misinformation and its wide-ranging consequences.
Emulating Reader Behaviors for Fake News Detection
To this end, we propose an approach of Emulating the behaviors of readers (Ember) for fake news detection on social media, incorporating readers' reading and verificating process to model news from the component perspective thoroughly.
Data Augmentation for Seizure Prediction with Generative Diffusion Model
The randomness of input noise and the precise representation enable the synthetic samples to possess diversity while ensuring the consistency of feature space.
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection
However, the work exploring limited labeled anomalies and a large amount of unlabeled nodes in graphs to detect anomalies is relatively limited.
TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks
To bridge this gap, we have constructed a real-world graph-based Traffic Accident Prediction (TAP) data repository, along with two representative tasks: accident occurrence prediction and accident severity prediction.
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society.
Ranked #1 on
Graph Classification
on UPFD-POL
Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues
Because of the uniqueness and importance of combating health misinformation in social media, we conduct this survey to further facilitate interdisciplinary research on this problem.
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Thus, it is essential to ensure fairness in machine learning models.
Memory-Guided Multi-View Multi-Domain Fake News Detection
In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach
Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes.
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
Extensive experiment results on few-shot text classification tasks demonstrate the superior performance of the proposed framework by effectively leveraging label semantics and data augmentation for natural language understanding.
Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.
Domain Adaptive Fake News Detection via Reinforcement Learning
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news.
"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders
To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading.
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach
In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features.
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding
In this paper, we propose such a benchmark, named WALNUT (semi-WeAkly supervised Learning for Natural language Understanding Testbed), to advocate and facilitate research on weak supervision for NLU.
Labeled Data Generation with Inexact Supervision
We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks by learning from data with inexact supervision and the relations between inexact supervision and target classes.
Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features
Though the sensitive attribute of each data sample is unknown, we observe that there are usually some non-sensitive features in the training data that are highly correlated with sensitive attributes, which can be used to alleviate the bias.
User Preference-aware Fake News Detection
The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.
Ranked #1 on
Graph Classification
on UPFD-GOS
Fact-Enhanced Synthetic News Generation
The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy.
MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two.
Social and Information Networks Computers and Society
Fake News Detection through Graph Comment Advanced Learning
Yet, the popularity of social media also provides opportunities to better detect fake news.
Fake News Detection
Representation Learning
Social and Information Networks
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach
In this paper, we consider realistic news as news that cannot be easily detected by a fake news classifier.
Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges
We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.
Combating Disinformation in a Social Media Age
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.
Learning with Weak Supervision for Email Intent Detection
In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails.
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
Social media has greatly enabled people to participate in online activities at an unprecedented rate.
Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media.
Deep causal representation learning for unsupervised domain adaptation
Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.
Detecting Fake News with Weak Social Supervision
This is especially the case for many real-world tasks where large scale annotated examples are either too expensive to acquire or unavailable due to privacy or data access constraints.
Applications of Social Media in Hydroinformatics: A Survey
Floods of research and practical applications employ social media data for a wide range of public applications, including environmental monitoring, water resource managing, disaster and emergency response. Hydroinformatics can benefit from the social media technologies with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper first proposes a 4W (What, Why, When, hoW) model and a methodological structure to better understand and represent the application of social media to hydroinformatics, then provides an overview of academic research of applying social media to hydroinformatics such as water environment, water resources, flood, drought and water Scarcity management.
The Role of User Profile for Fake News Detection
In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news.
Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation
In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection.
Social and Information Networks
Graph Neural Networks for User Identity Linkage
In particular, we provide a principled approach to jointly capture local and global information in the user-user social graph and propose the framework {\m}, which jointly learning user representations for user identity linkage.
Mining Dual Emotion for Fake News Detection
Emotion plays an important role in detecting fake news online.
FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media
However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information.
Social and Information Networks
Exploiting Tri-Relationship for Fake News Detection
Recent Social and Psychology studies show potential importance to utilize social media data: 1) Confirmation bias effect reveals that consumers prefer to believe information that confirms their existing stances; 2) Echo chamber effect suggests that people tend to follow likeminded users and form segregated communities on social media.
Social and Information Networks
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications
We apply our solution to sentiment analysis, a task that can benefit from the emoji calibration technique we use in this work.
Fake News Detection on Social Media: A Data Mining Perspective
First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.
