{"total":50,"rows":[{"id":"2308.05374","title":"Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment","created":"2023-08-10","cited_by_count":401,"area":null},{"id":"2312.02003","title":"A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly","created":"2023-12-04","cited_by_count":387,"area":null},{"id":"2307.10169","title":"Challenges and Applications of Large Language Models","created":"2023-07-19","cited_by_count":254,"area":null},{"id":"2307.01850","title":"Self-Consuming Generative Models Go MAD","created":"2023-07-04","cited_by_count":250,"area":null},{"id":"2310.05189","title":"Factuality Challenges in the Era of Large Language Models","created":"2023-10-08","cited_by_count":126,"area":null},{"id":"2310.14724","title":"A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions","created":"2023-10-23","cited_by_count":105,"area":null},{"id":"2310.05914","title":"NEFTune: Noisy Embeddings Improve Instruction Finetuning","created":"2023-10-09","cited_by_count":100,"area":null},{"id":"2308.14840","title":"Identifying and Mitigating the Security Risks of Generative AI","created":"2023-08-28","cited_by_count":91,"area":null},{"id":"2402.02333","title":"Copyright Protection in Generative AI: A Technical Perspective","created":"2024-02-04","cited_by_count":38,"area":null},{"id":"2403.00025","title":"On the Challenges and Opportunities in Generative AI","created":"2024-02-28","cited_by_count":31,"area":null},{"id":"2310.15264","title":"Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey","created":"2023-10-23","cited_by_count":28,"area":null},{"id":"2410.07959","title":"COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act","created":"2024-10-10","cited_by_count":24,"area":null},{"id":"2501.17858","title":"Improving Your Model Ranking on Chatbot Arena by Vote Rigging","created":"2025-01-29","cited_by_count":17,"area":null},{"id":"2406.02633","title":"Edit Distance Robust Watermarks via Indexing Pseudorandom Codes","created":"2024-06-04","cited_by_count":13,"area":null},{"id":"2406.04662","title":"Evaluating and Mitigating IP Infringement in Visual Generative AI","created":"2024-06-07","cited_by_count":13,"area":null},{"id":"2502.05240","title":"Survey on AI-Generated Media Detection: From Non-MLLM to MLLM","created":"2025-02-07","cited_by_count":12,"area":null},{"id":"2510.17489","title":"DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning","created":"2025-10-20","cited_by_count":12,"area":null},{"id":"2309.07689","title":"Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text","created":"2023-09-14","cited_by_count":10,"area":null},{"id":"2504.13416","title":"STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings","created":"2025-04-18","cited_by_count":8,"area":null},{"id":"2405.06331","title":"LMD3: Language Model Data Density Dependence","created":"2024-05-10","cited_by_count":7,"area":null},{"id":"2405.13517","title":"WaterPool: A Watermark Mitigating Trade-offs among Imperceptibility, Efficacy and Robustness","created":"2024-05-22","cited_by_count":6,"area":null},{"id":"2409.14285","title":"ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination","created":"2024-09-22","cited_by_count":6,"area":null},{"id":"2502.17259","title":"Detecting Benchmark Contamination Through Watermarking","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2310.01297","title":"Co-audit: tools to help humans double-check AI-generated content","created":"2023-10-02","cited_by_count":5,"area":null},{"id":"2311.07700","title":"AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising","created":"2023-11-13","cited_by_count":5,"area":null},{"id":"2509.07287","title":"Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm","created":"2025-09-08","cited_by_count":5,"area":null},{"id":"2312.01523","title":"SymNoise: Advancing Language Model Fine-tuning with Symmetric Noise","created":"2023-12-03","cited_by_count":4,"area":null},{"id":"2510.09655","title":"Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique","created":"2025-10-07","cited_by_count":4,"area":null},{"id":"2406.03728","title":"Evaluating Durability: Benchmark Insights into Multimodal Watermarking","created":"2024-06-06","cited_by_count":3,"area":null},{"id":"2501.03940","title":"Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection","created":"2025-01-07","cited_by_count":3,"area":null},{"id":"2507.06489","title":"On the Robustness of Verbal Confidence of LLMs in Adversarial Attacks","created":"2025-07-09","cited_by_count":3,"area":null},{"id":"2604.06662","title":"Towards Robust Content Watermarking Against Removal and Forgery Attacks","created":"2026-04-08","cited_by_count":3,"area":null},{"id":"2510.09210","title":"Provable Watermarking for Data Poisoning Attacks","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2510.16830","title":"Verifiable Fine-Tuning for LLMs: Zero-Knowledge Training Proofs Bound to Data Provenance and Policy","created":"2025-10-19","cited_by_count":2,"area":null},{"id":"2605.13095","title":"Watermarking Should Be Treated as a Monitoring Primitive","created":"2026-05-13","cited_by_count":2,"area":null},{"id":"2311.01193","title":"Contextual Confidence and Generative AI","created":"2023-11-02","cited_by_count":1,"area":null},{"id":"2504.02898","title":"A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content","created":"2025-04-02","cited_by_count":1,"area":null},{"id":"2506.20197","title":"Zero-Shot Attribution for Large Language Models: A Distribution Testing Approach","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2511.00973","title":"Keys in the Weights: Transformer Authentication Using Model-Bound Latent Representations","created":"2025-11-02","cited_by_count":1,"area":null},{"id":"2601.20006","title":"On the Effectiveness of LLM-Specific Fine-Tuning for Detecting AI-Generated Text","created":"2026-01-27","cited_by_count":1,"area":null},{"id":"2604.26328","title":"DSIPA: Detecting LLM-Generated Texts via Sentiment-Invariant Patterns Divergence Analysis","created":"2026-04-29","cited_by_count":1,"area":null},{"id":"2407.07225","title":"ConvNLP: Image-based AI Text Detection","created":"2024-07-09","cited_by_count":0,"area":null},{"id":"2502.05425","title":"Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking for Intelligent Transportation Systems","created":"2025-02-08","cited_by_count":0,"area":null},{"id":"2506.22623","title":"Temperature Matters: Enhancing Watermark Robustness Against Paraphrasing Attacks","created":"2025-06-27","cited_by_count":0,"area":null},{"id":"2512.06922","title":"Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI","created":"2025-12-07","cited_by_count":0,"area":null},{"id":"2605.06865","title":"Dataset Watermarking with Provable Black-Box Detection","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.23190","title":"Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.30219","title":"EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2607.10210","title":"Preserving Structure, Strengthening Signals: A Grammar-Driven Approach to Optimize the Trade-off Between Quality and Detectability in Code Watermarking","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2607.14967","title":"Latent Trajectory Discrimination for AI-Generated Text Detection","created":"2026-07-16","cited_by_count":0,"area":null}],"next_cursor":null}