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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 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 AIDetection methods Disinformation Capabilities of Large Language ModelsComprehensive empirical evaluation of 10 LLMs' ability to generate disinformation news articles. Most models readily generate convincing disinformation that agrees with dangerous narratives; Falcon is the sole exception. Vykopal et al. · arXiv · 2023

Detection methodsLLMs & generative AI 3HAN: A Deep Neural Network for Fake News DetectionProposes 3HAN, a three-level hierarchical attention network for fake news detection that models articles as a hierarchy of words, sentences, and headlines. The model assigns differential importance weights to different parts via attention mechanisms, achieving 96.77% accuracy while providing interpretable attention visualizations showing… Singhania et al. · arXiv · 2023

LLMs & generative AIDetection methods Can AI-Generated Text be Reliably Detected?This paper stress-tests AI-text detectors using a novel recursive paraphrasing attack, showing that watermarking-based, neural network-based, and retrieval-based detectors can be reliably broken with only minor text degradation. The work establishes a theoretical connection between detector AUROC and the total variation distance between… Sadasivan et al. · arXiv · 2023

LLMs & generative AIDetection methods On the Risk of Misinformation Pollution with Large Language ModelsThis paper investigates how modern LLMs can be misused to generate convincing misinformation at scale and its impact on downstream NLP applications. Using GPT-3.5 as a misinformation generator, the authors demonstrate that synthetically-generated false information can significantly degrade ODQA system performance (up to 87% in some… Pan et al. · arXiv · 2023

LLMs & generative AIDetection methods DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability CurvatureThis paper identifies that text sampled from language models tends to occupy negative-curvature regions of the model's log-probability landscape, whereas human-written text occupies more neutral regions. DetectGPT leverages this observation to detect machine-generated text in a zero-shot manner by analyzing probability curvature, without… Mitchell et al. · 40th International Conference on Machine… · 2023

LLMs & generative AIDetection methods A Watermark for Large Language ModelsProposes a watermarking scheme for large language models that embeds imperceptible signals into generated text, enabling detection of machine-generated content without access to model parameters or APIs. The watermark works by promoting a randomized set of "green" tokens during decoding and can be detected using a statistical z-test; the… Kirchenbauer et al. · arXiv · 2023

LLMs & generative AIDetection methods Combating Misinformation in the Age of LLMs: Opportunities and ChallengesThis survey systematically reviews how large language models (LLMs) can both combat misinformation and generate it at scale. LLMs offer opportunities for detection, intervention, and attribution through their world knowledge and reasoning abilities, but also pose challenges as they can create deceptive content intentionally or through… Chen & Shu · arXiv · 2023

Detection methodsLLMs & generative AI Linguistic-style-aware Neural Networks for Fake News DetectionHERO (Hierarchical Recursive Neural Network) detects fake news by learning the linguistic style encoded in hierarchical linguistic trees — structures that jointly capture syntactic constituency and rhetorical discourse organization from word to document. Unlike prior handcrafted approaches that extract node-level statistics (word… Zhou et al. · arXiv · 2023

LLMs & generative AIDetection methods All the News That's Fit to Fabricate: AI-Generated Text as a Tool of Media MisinformationThree experiments investigate whether AI-generated news text can be credible and influence public opinion. The authors find that GPT-2 models (355M to 1.5B parameters) generate text nearly indistinguishable from human-written baseline news, particularly when politically congruent stories are paired with partisan audiences. Kreps et al. · Journal of Experimental Political Science · 2022

LLMs & generative AIFact-checking & verification Atlas: Few-shot Learning with Retrieval Augmented Language ModelsATLAS is a retrieval-augmented language model that achieves strong few-shot performance on knowledge-intensive tasks by jointly training a dense retriever with a sequence-to-sequence language model. Unlike standard language models that must memorize knowledge, ATLAS retrieves relevant documents at both pre-training and fine-tuning… Izacard et al. · arXiv · 2022

LLMs & generative AIDetection methods Discovering Latent Knowledge in Language Models Without SupervisionLanguage models trained with standard objectives (imitation, reward-based) can output false statements even when they internally "know" the truth. This paper proposes Contrast-Consistent Search (CCS), an unsupervised method that exploits truth's logical structure—a statement and its negation cannot both be true—to recover latent… Burns et al. · arXiv · 2022

LLMs & generative AIBots & coordinated campaigns Red Teaming Language Models with Language ModelsThis paper demonstrates that language models can automatically discover a wide range of harmful behaviors in other language models through "red teaming"—using one LM to generate adversarial test cases that probe another LM's vulnerabilities. The authors explore multiple generation strategies (zero-shot, few-shot, supervised learning,… Perez et al. · arXiv · 2022

LLMs & generative AIDatasets & evaluation Discovering Language Model Behaviors with Model-Written EvaluationsRather than manually creating evaluation datasets, this work proposes using language models themselves to generate high-quality evaluations for testing diverse LM behaviors. The authors generate 154 datasets testing behaviors spanning personality, political views, ethics, and goal-seeking. Perez et al. · 2022

LLMs & generative AIDetection methods Causal Machine Learning: A Survey and Open ProblemsComprehensive survey of causal machine learning (CausalML), which formalizes data generation as a structural causal model to reason about interventions and counterfactuals. Categorizes 191 pages of CausalML work into five groups—causal supervised learning, causal generative modeling, causal explanations, causal fairness, and causal… Kaddour et al. · arXiv · 2022

Platforms, policy & societyDetection methods A Comprehensive Survey on Trustworthy Recommender SystemsRecommender systems, while beneficial to users, can amplify harmful content, introduce discrimination, and enable adversarial attacks. This survey provides a comprehensive framework for trustworthy recommender systems across six critical dimensions: Safety & Robustness (defense against adversarial attacks), Non-discrimination & Fairness… Fan et al. · ACM Computing Surveys · 2022

Detection methodsLLMs & generative AI Toxicity Detection with Generative Prompt-based InferenceThis paper proposes a generative variant of zero-shot prompt-based toxicity detection using language models, comparing it against discriminative approaches. By formulating the problem as estimating the probability of generating a test instance conditioned on toxicity-guided prompts, the method achieves better performance than… Wang & Chang · arXiv · 2022

LLMs & generative AIDetection methods Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated TextDugan et al. investigate human ability to detect boundaries where text transitions from human-written to machine-generated, a more realistic and challenging task than binary fake/real classification. They introduce the RoFT (Real or Fake Text) game platform for crowdsourced annotation and release a dataset of 21,000+ annotations across… Dugan et al. · arXiv · 2022

Fact-checking & verificationDetection methods A Survey on Automated Fact-CheckingThis comprehensive survey unifies automated fact-checking research under a four-stage NLP framework: claim detection (identifying check-worthy claims), evidence retrieval (finding supporting/refuting information), verdict prediction (assigning truth labels), and justification production (explaining decisions). The authors review 25+… Guo et al. · arXiv · 2021

Detection methodsLLMs & generative AI Belief Evolution Network-based Probability Transformation and FusionThis paper introduces a Belief Evolution Network (BEN) to model the evolution of belief functions and proposes a new Full Causality Probability Transformation (FCPT) method for converting belief mass assignments to probability distributions. The authors also propose FCPT-based fusion rules that outperform traditional Dempster's Rule of… Zhou et al. · arXiv · 2021

Deepfakes & multimodalDetection methods Multimodal Emergent Fake News Detection via Meta Neural Process NetworksExisting fake news detectors trained on labeled data perform poorly on novel, emergent events due to domain shift. The paper proposes MetaFEND, which combines meta-learning and neural processes to leverage a handful of labeled posts from a new event to quickly adapt a detection model. Wang et al. · ACM SIGKDD Conference on Knowledge Discovery… · 2021

Detection methodsLLMs & generative AI Perceived and Intended Sarcasm Detection with Graph Attention NetworksSarcasm detection requires understanding both the author's intent and the audience's perception. This work proposes a graph attention network (GAT) that jointly models user historical context and conversational social network information on Twitter, achieving state-of-the-art results on the SPIRS dataset and showing that detecting… Plepi & Flek · arXiv · 2021

Detection methodsDatasets & evaluation Misinformation Detection on YouTube Using Video CaptionsThis work proposes using video captions as text features for classifying YouTube videos as misinformation, debunking misinformation, or neutral content. Across five conspiracy topics (vaccines, 9/11, chemtrails, moon landing, flat earth), the authors achieve 0.85–0.90 F1-score on three-class classification and 0.92–0.95 F1-score when… Jagtap et al. · arXiv · 2021