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Found 21 papers, 9 papers with code
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
Ranked #2 on
Mathematical Reasoning
on AIME24
DeepSeek-V3 Technical Report
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
Assessing the Impact of Conspiracy Theories Using Large Language Models
Measuring the relative impact of CTs is important for prioritizing responses and allocating resources effectively, especially during crises.
A Multi-Scale Spatial-Temporal Network for Wireless Video Transmission
Deep joint source-channel coding (DeepJSCC) has shown promise in wireless transmission of text, speech, and images within the realm of semantic communication.
Channel-Adaptive Wireless Image Semantic Transmission with Learnable Prompts
Recent developments in Deep learning based Joint Source-Channel Coding (DeepJSCC) have demonstrated impressive capabilities within wireless semantic communications system.
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation
We explicitly train the model to learn graph conditioning with a condition loss, which enhances the diffusion model's capacity to generate graphs that are both realistic and aligned with specified properties.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
Correcting misinformation on social media with a large language model
High-quality and timely correction of misinformation that identifies and explains its (in)accuracies effectively reduces false beliefs.
ChatGPT is a Potential Zero-Shot Dependency Parser
Pre-trained language models have been widely used in dependency parsing task and have achieved significant improvements in parser performance.
Temporal Interest Network for User Response Prediction
To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target.
Linguistic-style-aware Neural Networks for Fake News Detection
We propose the hierarchical recursive neural network (HERO) to predict fake news by learning its linguistic style, which is distinguishable from the truth, as psychological theories reveal.
From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection
The COVID-19 pandemic has gained worldwide attention and allowed fake news, such as ``COVID-19 is the flu,'' to spread quickly and widely on social media.
"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.
CHECKED: Chinese COVID-19 Fake News Dataset
Also, misinformation on COVID-19 is frequently spread on social media.
ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research
Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies.
SAFE: Similarity-Aware Multi-Modal Fake News Detection
Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles.
Credibility-based Fake News Detection
By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility.
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.
Fake News Early Detection: An Interdisciplinary Study
Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.
Fake News: A Survey of Research, Detection Methods, and Opportunities
The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention.
A DEMATEL-Based Completion Method for Incomplete Pairwise Comparison Matrix in AHP
However, in many cases, the pairwise comparison matrix is difficult to complete, which obstructs the subsequent operations of the clas- sical AHP.
