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2023
The Science of Detecting LLM-Generated Texts
Tang et al. · arXiv · LLMs & generative AI, Detection methods2023
Adapting Fake News Detection to the Era of Large Language Models
Su et al. · arXiv · LLMs & generative AI, Detection methods2023
The Amplification Paradox in Recommender Systems
Ribeiro et al. · ICWSM · Spread & networks, Platforms, policy & society2023
The Perils & Promises of Fact-checking with Large Language Models
Quelle & Bovet · arXiv · Fact-checking & verification, LLMs & generative AI2023
Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media
Mills et al. · arXiv · Spread & networks, Platforms, policy & society2023
FLIRT: Feedback Loop In-context Red Teaming
Mehrabi et al. · arXiv · LLMs & generative AI, Deepfakes & multimodal2023
Disinformation 2.0 in the Age of AI: A Cybersecurity Perspective
Mazurczyk et al. · ACM Publication · Platforms, policy & society, LLMs & generative AI2023
Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' Alignment
Liu et al. · arXiv · LLMs & generative AI, Platforms, policy & society2023
Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations
Goldstein et al. · arXiv · LLMs & generative AI, Platforms, policy & society2023
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Deshpande et al. · arXiv · LLMs & generative AI, Platforms, policy & society2023
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Dai et al. · ACM Computing Surveys · LLMs & generative AI, Detection methods2023
A Survey on Evaluation of Large Language Models
Chang et al. · Journal of the ACM · LLMs & generative AI, Datasets & evaluation2023
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
Cao et al. · J. ACM, Vol. 37, No. 4 · LLMs & generative AI, Deepfakes & multimodal2023
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
Ayoobi et al. · 3rd ACM Conference on Hypertext and Social Media · LLMs & generative AI, Datasets & evaluation2023
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Asai et al. · arXiv · LLMs & generative AI, Detection methods2023
Disinformation Capabilities of Large Language Models
Vykopal et al. · arXiv · LLMs & generative AI, Detection methods2023
3HAN: A Deep Neural Network for Fake News Detection
Singhania et al. · arXiv · Detection methods, LLMs & generative AI2023
Can AI-Generated Text be Reliably Detected?
Sadasivan et al. · arXiv · LLMs & generative AI, Detection methods2023
On the Risk of Misinformation Pollution with Large Language Models
Pan et al. · arXiv · LLMs & generative AI, Detection methods2023
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Mitchell et al. · 40th International Conference on Machine… · LLMs & generative AI, Detection methods2023
A Watermark for Large Language Models
Kirchenbauer et al. · arXiv · LLMs & generative AI, Detection methods2023
Combating Misinformation in the Age of LLMs: Opportunities and Challenges
Chen & Shu · arXiv · LLMs & generative AI, Detection methods2023
Linguistic-style-aware Neural Networks for Fake News Detection
Zhou et al. · arXiv · Detection methods, LLMs & generative AI2022
From Fake News to #FakeNews: Mining Direct and Indirect Relationships among Hashtags for Fake News Detection
Zhou et al. · ACM Conference · Detection methods, Datasets & evaluation