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Psychology & interventionsDetection methods Prebunking Misinformation on Social MediaJon Roozenbeek presents the case for prebunking—teaching people to recognize manipulation tactics before encountering false claims—rather than relying on reactive debunking. Drawing on inoculation theory from the 1960s, he argues that manipulation techniques recur across misinformation campaigns: emotional exploitation, false… Roozenbeek · NNLM Virtual Symposium on Health Misinformation · 2023

Psychology & interventionsFact-checking & verification Psychological Inoculation Against MisinformationSander van der Linden introduces psychological inoculation as a scalable alternative to fact-checking for building societal resistance to misinformation. Drawing on inoculation theory and his flagship intervention, the Bad News game, he demonstrates how preemptively exposing people to weakened examples of six core manipulation techniques… van der Linden · CPFT Research · 2023

LLMs & generative AI A Survey of Large Language ModelsThis comprehensive survey reviews the evolution of large language models (LLMs) from statistical language models through pre-trained models to contemporary transformer-based LLMs. The authors examine four major dimensions: pre-training strategies, parameter-efficient adaptation methods, utilization approaches (prompting and in-context… Zhao et al. · arXiv · 2023

LLMs & generative AIDetection methods Self-RAG: Learning to Retrieve, Generate, and Critique through Self-ReflectionThis paper introduces SELF-RAG (Self-Reflective Retrieval-Augmented Generation), a framework that teaches language models to adaptively decide when to retrieve external passages, generate factually grounded responses, and critique their own outputs. Using special "reflection tokens," the model learns to self-evaluate at inference time… Asai et al. · arXiv · 2023

LLMs & generative AIDatasets & evaluation The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and PreventionLinkedIn's lack of verification enables fake accounts that harm legitimate users and the platform's integrity. This work introduces a dataset of 3600 profiles (legitimate, manually created fake, and ChatGPT-generated) and proposes SSTE, a method that detects fake profiles immediately after registration using only profile text. Ayoobi et al. · 3rd ACM Conference on Hypertext and Social Media · 2023

LLMs & generative AIDeepfakes & multimodal A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTThis is the first comprehensive survey of AI-Generated Content (AIGC) covering the history, techniques, and applications of generative models from GANs through ChatGPT. It addresses both the impressive capabilities of these models (text, image, audio, video generation) and critical concerns including factuality, security, privacy, and… Cao et al. · J. ACM, Vol. 37, No. 4 · 2023

LLMs & generative AIDatasets & evaluation A Survey on Evaluation of Large Language ModelsThis survey provides a comprehensive review of evaluation methodologies for large language models across three dimensions: what to evaluate, where to evaluate, and how to evaluate. The paper encompasses evaluation tasks spanning natural language processing, natural language generation, robustness, ethics, bias detection, trustworthiness,… Chang et al. · Journal of the ACM · 2023

LLMs & generative AIDetection methods A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and ExplainabilityA comprehensive survey of trustworthy Graph Neural Networks covering four critical dimensions: privacy attacks and defenses against membership/property inference and model extraction; robustness against adversarial perturbations; fairness mechanisms to prevent discrimination in graph-based predictions; and explainability methods to… Dai et al. · ACM Computing Surveys · 2023

LLMs & generative AIPlatforms, policy & society Toxicity in ChatGPT: Analyzing Persona-assigned Language ModelsLarge-scale empirical analysis of ChatGPT's toxicity across 90 personas using 500K+ generations. Assigning personas via the system parameter increases toxicity up to 6×, with variation depending on persona characteristics and target entities. Deshpande et al. · arXiv · 2023

LLMs & generative AIPlatforms, policy & society Generative Language Models and Automated Influence Operations: Emerging Threats and Potential MitigationsGenerative language models like GPT-3 and ChatGPT could significantly expand the scale and scope of influence operations by automating high-quality propaganda generation, lowering barriers to entry for new propagandists, and enabling novel tactics. The authors use an Actor-Behavior-Content (ABC) framework to identify how these models… Goldstein et al. · arXiv · 2023

LLMs & generative AIPlatforms, policy & society Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' AlignmentThis survey provides a comprehensive framework for evaluating LLM trustworthiness across seven dimensions: reliability, safety, fairness, resistance to misuse, explainability, social norms, and robustness. The authors present a detailed taxonomy with 29 sub-categories, conduct empirical measurements on widely-used LLMs (GPT-4, ChatGPT,… Liu et al. · arXiv · 2023

Platforms, policy & societyLLMs & generative AI Disinformation 2.0 in the Age of AI: A Cybersecurity PerspectiveDisinformation should be treated as a cybersecurity threat, particularly as AI advances enable its creation and dissemination at unprecedented scale. The paper introduces "disinformation 2.0"—where generative AI, perturbation techniques, detection evasion, and optimized spread strategies make false content more persuasive and harder to… Mazurczyk et al. · ACM Publication · 2023

LLMs & generative AIDeepfakes & multimodal FLIRT: Feedback Loop In-context Red TeamingFLIRT automates red teaming of generative models by using in-context learning to generate adversarial prompts in a feedback loop: a red language model generates candidate prompts, they are evaluated against a target model (e.g., text-to-image or text-to-text), safety classifiers assess the outputs, and results feed back to refine future… Mehrabi et al. · arXiv · 2023

Spread & networksPlatforms, policy & society Engagement, User Satisfaction, and the Amplification of Divisive Content on Social MediaTwitter's engagement-based ranking algorithm amplifies emotionally charged and partisan divisive content beyond what users report wanting to see. Through a pre-registered experiment comparing the engagement-based timeline to a reverse-chronological baseline, the authors show the algorithm increases anger, sadness, and out-group… Mills et al. · arXiv · 2023

Fact-checking & verificationLLMs & generative AI The Perils & Promises of Fact-checking with Large Language ModelsThis paper evaluates GPT-3.5 and GPT-4's ability to fact-check claims across English-only and multilingual datasets, showing GPT-4 substantially outperforms GPT-3.5 but both models show inconsistent accuracy when dealing with ambiguous verdicts. Critically, incorporating contextual information via Google Search significantly improves… Quelle & Bovet · arXiv · 2023

Spread & networksPlatforms, policy & society The Amplification Paradox in Recommender SystemsAlgorithmic audits find that blindly following YouTube recommendations leads to extreme content, yet real user logs show recommendations don't drive extreme content consumption. Using an agent-based model with realistic user preferences, the authors show this paradox arises because users rarely consume niche content even when recommended… Ribeiro et al. · ICWSM · 2023

LLMs & generative AIDetection methods Adapting Fake News Detection to the Era of Large Language ModelsAs large language models proliferate and machine-generated text becomes prevalent, traditional fake news detectors trained exclusively on human-written content are increasingly unreliable. This paper evaluates fake news detectors across three hypothetical eras—human-written dominance, mixed human-machine content, and machine-generated… Su et al. · arXiv · 2023

LLMs & generative AIDetection methods The Science of Detecting LLM-Generated TextsThis survey comprehensively reviews techniques for detecting LLM-generated text, a critical problem given concerns about misinformation, academic dishonesty, and unauthorized use. The authors categorize detection approaches into black-box (API-level access) and white-box (full model access) methods, discuss watermarking strategies,… Tang et al. · arXiv · 2023

Spread & networksDetection methods The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A SurveyThis survey systematically examines distributed systems for scalable GNN training, bridging graph processing systems (Pregel, PowerGraph, GraphLab) and DNN training systems (TensorFlow, PyTorch) to show how techniques from both domains inform GNN system design. The paper categorizes methods across partitioning, sampling, communication,… Vatter et al. · arXiv · 2023

LLMs & generative AIFact-checking & verification Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityThis comprehensive survey addresses the critical problem of factuality in large language models, examining how LLMs store and retrieve knowledge, analyzing the mechanisms through which they generate false or misleading content, and reviewing evaluation metrics and enhancement strategies. The paper distinguishes factuality issues from… Wang et al. · arXiv · 2023

LLMs & generative AIPlatforms, policy & society Jailbroken: How Does LLM Safety Training Fail?This paper investigates why large language models trained for safety remain vulnerable to jailbreak attacks. Through analysis of models including GPT-4 and Claude v1.3, the authors identify two key failure modes: competing objectives (where safety constraints conflict with other capabilities) and mismatched generalization (where safety… Wei et al. · arXiv · 2023

LLMs & generative AIDetection methods A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future DirectionsAs LLMs become ubiquitous, detecting machine-generated text is critical for mitigating risks of misinformation, academic dishonesty, and misuse. This survey comprehensively reviews detection strategies—watermarking techniques, statistical methods, neural-based detectors, and human-assisted approaches—along with datasets, evaluation… Wu et al. · arXiv · 2023

LLMs & generative AIDatasets & evaluation How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent AdvancesThis survey reviews methods for aligning large language models with ever-changing world knowledge without retraining from scratch. The authors systematically categorize approaches as either implicit (modifying LLM parameters directly via knowledge editing or continual learning) or explicit (augmenting LLMs with external resources via… Zhang et al. · arXiv · 2023

LLMs & generative AIPlatforms, policy & society Universal and Transferable Adversarial Attacks on Aligned Language ModelsDespite alignment training, modern language models can be induced to generate harmful content via automatically-discovered adversarial suffixes. The authors propose Greedy Coordinate Gradient (GCG) search, which generates a single universal suffix that transfers across multiple models and prompts with high success rates (88% on… Zou et al. · arXiv · 2023