{"list":"other","total":98,"rows":[{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2005.11401","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","created":"2020-05-22","cited_by_count":9297},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"2202.03629","title":"Survey of Hallucination in Natural Language Generation","created":"2022-02-08","cited_by_count":2075},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"id":"2112.04426","title":"Improving language models by retrieving from trillions of tokens","created":"2021-12-08","cited_by_count":1293},{"id":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"id":"1708.06733","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","created":"2017-08-22","cited_by_count":1106},{"id":"1712.05526","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","created":"2017-12-15","cited_by_count":1055},{"id":"2310.13548","title":"Towards Understanding Sycophancy in Language Models","created":"2023-10-20","cited_by_count":1040},{"id":"2302.00093","title":"Large Language Models Can Be Easily Distracted by Irrelevant Context","created":"2023-01-31","cited_by_count":841},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"2211.09527","title":"Ignore Previous Prompt: Attack Techniques For Language Models","created":"2022-11-17","cited_by_count":832},{"id":"1511.07528","title":"The Limitations of Deep Learning in Adversarial Settings","created":"2015-11-24","cited_by_count":790},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"2309.00770","title":"Bias and Fairness in Large Language Models: A Survey","created":"2023-09-02","cited_by_count":639},{"id":"1609.02943","title":"Stealing Machine Learning Models via Prediction APIs","created":"2016-09-09","cited_by_count":619},{"id":"2306.05499","title":"Prompt Injection attack against LLM-integrated Applications","created":"2023-06-08","cited_by_count":605},{"id":"1804.00792","title":"Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks","created":"2018-04-03","cited_by_count":519},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"1902.06705","title":"On Evaluating Adversarial Robustness","created":"2019-02-18","cited_by_count":447},{"id":"2007.08745","title":"Backdoor Learning: A Survey","created":"2020-07-17","cited_by_count":426},{"id":"2404.14082","title":"Mechanistic Interpretability for AI Safety -- A Review","created":"2024-04-22","cited_by_count":422},{"id":"1712.03141","title":"Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning","created":"2017-12-08","cited_by_count":414},{"id":"2410.02736","title":"Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge","created":"2024-10-03","cited_by_count":368},{"id":"1706.03691","title":"Certified Defenses for Data Poisoning Attacks","created":"2017-06-09","cited_by_count":303},{"id":"2007.08432","title":"Data Poisoning Attacks Against Federated Learning Systems","created":"2020-07-16","cited_by_count":275},{"id":"1702.04267","title":"On Detecting Adversarial Perturbations","created":"2017-02-14","cited_by_count":232},{"id":"2310.01783","title":"Can large language models provide useful feedback on research papers? A large-scale empirical analysis","created":"2023-10-03","cited_by_count":200},{"id":"1708.08689","title":"Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization","created":"2017-08-29","cited_by_count":197},{"id":"2310.10844","title":"Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks","created":"2023-10-16","cited_by_count":194},{"id":"2012.10544","title":"Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses","created":"2020-12-18","cited_by_count":180},{"id":"2103.11790","title":"Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do","created":"2021-03-08","cited_by_count":178},{"id":"2403.07183","title":"Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews","created":"2024-03-11","cited_by_count":164},{"id":"2003.12862","title":"Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning","created":"2020-03-28","cited_by_count":153},{"id":"2309.17012","title":"Benchmarking Cognitive Biases in Large Language Models as Evaluators","created":"2023-09-29","cited_by_count":150},{"id":"2411.00816","title":"CycleResearcher: Improving Automated Research via Automated Review","created":"2024-10-28","cited_by_count":148},{"id":"2403.17710","title":"Optimization-based Prompt Injection Attack to LLM-as-a-Judge","created":"2024-03-26","cited_by_count":143},{"id":"2409.13740","title":"Language agents achieve superhuman synthesis of scientific knowledge","created":"2024-09-10","cited_by_count":137},{"id":"2402.14016","title":"Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment","created":"2024-02-21","cited_by_count":126},{"id":"2306.00622","title":"ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing","created":"2023-06-01","cited_by_count":122},{"id":"2402.15938","title":"Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models","created":"2024-02-24","cited_by_count":122},{"id":"2006.12557","title":"Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks","created":"2020-06-22","cited_by_count":116},{"id":"2310.15140","title":"AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models","created":"2023-10-23","cited_by_count":111},{"id":"2312.09085","title":"The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation","created":"2023-12-14","cited_by_count":109},{"id":"2403.14380","title":"On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial","created":"2024-03-21","cited_by_count":109},{"id":"2502.08177","title":"SycEval: Evaluating LLM Sycophancy","created":"2025-02-12","cited_by_count":108},{"id":"2312.17673","title":"Jatmo: Prompt Injection Defense by Task-Specific Finetuning","created":"2023-12-29","cited_by_count":106},{"id":"2401.04259","title":"MARG: Multi-Agent Review Generation for Scientific Papers","created":"2024-01-08","cited_by_count":104},{"id":"2411.15287","title":"Sycophancy in Large Language Models: Causes and Mitigations","created":"2024-11-22","cited_by_count":101},{"id":"2406.12708","title":"AgentReview: Exploring Peer Review Dynamics with LLM Agents","created":"2024-06-18","cited_by_count":93},{"id":"2501.04306","title":"LLM4SR: A Survey on Large Language Models for Scientific Research","created":"2025-01-08","cited_by_count":83},{"id":"2411.10915","title":"Bias in Large Language Models: Origin, Evaluation, and Mitigation","created":"2024-11-16","cited_by_count":77},{"id":"2510.07192","title":"Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples","created":"2025-10-08","cited_by_count":72},{"id":"2507.01903","title":"AI4Research: A Survey of Artificial Intelligence for Scientific Research","created":"2025-07-02","cited_by_count":60},{"id":"2507.08794","title":"One Token to Fool LLM-as-a-Judge","created":"2025-07-11","cited_by_count":60},{"id":"2010.06119","title":"ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis","created":"2020-10-13","cited_by_count":54},{"id":"2412.01708","title":"Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review","created":"2024-12-02","cited_by_count":53},{"id":"2310.19737","title":"Adversarial Attacks and Defenses in Large Language Models: Old and New Threats","created":"2023-10-30","cited_by_count":49},{"id":"2410.13722","title":"Persistent Pre-Training Poisoning of LLMs","created":"2024-10-17","cited_by_count":48},{"id":"2311.09948","title":"Hijacking Large Language Models via Adversarial In-Context Learning","created":"2023-11-16","cited_by_count":45},{"id":"2110.07474","title":"MReD: A Meta-Review Dataset for Structure-Controllable Text Generation","created":"2021-10-14","cited_by_count":42},{"id":"2311.07954","title":"A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning","created":"2023-11-14","cited_by_count":41},{"id":"2307.05492","title":"GPT4 is Slightly Helpful for Peer-Review Assistance: A Pilot Study","created":"2023-06-16","cited_by_count":40},{"id":"2006.14042","title":"Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks","created":"2020-06-24","cited_by_count":37},{"id":"2407.12857","title":"Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis","created":"2024-07-09","cited_by_count":33},{"id":"2502.01386","title":"Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models","created":"2025-02-03","cited_by_count":33},{"id":"2408.02946","title":"Scaling Trends for Data Poisoning in LLMs","created":"2024-08-06","cited_by_count":30},{"id":"2305.01498","title":"Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation","created":"2023-05-02","cited_by_count":27},{"id":"2407.17291","title":"How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?","created":"2024-07-24","cited_by_count":27},{"id":"2503.01829","title":"Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Models","created":"2025-03-03","cited_by_count":27},{"id":"2409.16605","title":"Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications","created":"2024-09-25","cited_by_count":26},{"id":"2211.06398","title":"Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach","created":"2022-11-07","cited_by_count":23},{"id":"2503.22759","title":"Data Poisoning in Deep Learning: A Survey","created":"2025-03-27","cited_by_count":20},{"id":"2202.12273","title":"Matching Papers and Reviewers at Large Conferences","created":"2022-02-24","cited_by_count":18},{"id":"2503.00596","title":"BadJudge: Backdoor Vulnerabilities of LLM-as-a-Judge","created":"2025-03-01","cited_by_count":18},{"id":"2506.00794","title":"Predicting Empirical AI Research Outcomes with Language Models","created":"2025-06-01","cited_by_count":17},{"id":"2403.15529","title":"LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers","created":"2024-03-22","cited_by_count":16},{"id":"2404.17617","title":"Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning","created":"2024-04-26","cited_by_count":16},{"id":"2506.11113","title":"Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks","created":"2025-06-08","cited_by_count":16},{"id":"2305.14647","title":"Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation","created":"2023-05-24","cited_by_count":14},{"id":"2509.10248","title":"Prompt Injection Attacks on LLM Generated Reviews of Scientific Publications","created":"2025-09-12","cited_by_count":13},{"id":"2402.03530","title":"ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing","created":"2024-02-05","cited_by_count":12},{"id":"2402.10735","title":"Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification","created":"2024-02-16","cited_by_count":9},{"id":"2502.15427","title":"Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs","created":"2025-02-21","cited_by_count":9},{"id":"2101.02701","title":"Does double-blind peer-review reduce bias? Evidence from a top computer science conference","created":"2021-01-07","cited_by_count":8},{"id":"2502.12510","title":"Aspect-Guided Multi-Level Perturbation Analysis of Large Language Models in Automated Peer Review","created":"2025-02-18","cited_by_count":8},{"id":"2302.12611","title":"CARE: Collaborative AI-Assisted Reading Environment","created":"2023-02-24","cited_by_count":7},{"id":"2406.10294","title":"RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance","created":"2024-06-13","cited_by_count":7},{"id":"2406.07359","title":"GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews","created":"2024-06-11","cited_by_count":6},{"id":"2508.21675","title":"Is this chart lying to me? Automating the detection of misleading visualizations","created":"2025-08-29","cited_by_count":6},{"id":"2509.14189","title":"AI and the Future of Academic Peer Review","created":"2025-09-17","cited_by_count":4},{"id":"2506.20931","title":"SPA: Towards More Stealth and Persistent Backdoor Attacks in Federated Learning","created":"2025-06-26","cited_by_count":1}],"next_cursor":null}