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| Paper | Published | Area | Tags | Intel | Citations |
|---|---|---|---|---|---|
Search and browse arXiv CS/AI/ML papers, enriched with AI-generated insights.
Generate novel research ideas grounded in real arXiv papers with Brainstorm.
| Paper | Published | Area | Tags | Intel | Citations |
|---|---|---|---|---|---|
Search and browse arXiv CS/AI/ML papers, enriched with AI-generated insights.
Generate novel research ideas grounded in real arXiv papers with Brainstorm.
| Paper | Published | Area | Tags | Intel | Citations |
|---|---|---|---|---|---|
| Paper | Published | Area | Tags | Intel | Citations |
|---|---|---|---|---|---|
| Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks Jesse Thomason, Robin Jia, Ting-Yun Chang Published: 2023-11-15Area: Model EditingCitations: 26 Tags: ai-safety, benchmark, model-editing | 2023-11-15 | Model Editing | ai-safety, benchmark, model-editing | E5 / R3 (93%) | 26 |
| How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities Boshi Wang, Huan Sun, Lingbo Mo, Muhao Chen Published: 2023-11-15Area: Safety EvaluationCitations: 44 Tags: ai-safety, benchmark, safety-evaluation | 2023-11-15 | Safety Evaluation | ai-safety, benchmark, safety-evaluation | E5 / R3 (94%) | 44 |
| Identifying Linear Relational Concepts in Large Language Models Anthony Hunter, David Chanin, Oana-Maria Camburu Published: 2023-11-15Area: Representation AnalysisCitations: 8 Tags: ai-safety, empirical, representation-analysis | 2023-11-15 | Representation Analysis | ai-safety, empirical, representation-analysis | E5 / R3 (94%) | 8 |
| Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts Lichao Sun, Pan Zhou, Xiang Li, Yixin Liu Published: 2023-11-15Area: Multimodal SafetyCitations: 78 Tags: adversarial-robustness, ai-safety, empirical, multimodal-safety | 2023-11-15 | Multimodal Safety | adversarial-robustness, ai-safety, empirical, multimodal-safety | E4 / R3 (94%) | 78 |
| Bergeron: Combating Adversarial Attacks through a Conscience-Based Alignment Framework Abraham Sanders, Bingsheng Yao, Dakuo Wang, Matthew Pisano Published: 2023-11-16Area: Adversarial RobustnessCitations: 33 Tags: adversarial-robustness, ai-safety, alignment-training, empirical | 2023-11-16 | Adversarial Robustness | adversarial-robustness, ai-safety, alignment-training, empirical | E6 / R3 (94%) | 33 |
| Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking Bangzheng Li, Ben Zhou, Chaowei Xiao, Fei Wang Published: 2023-11-16Area: Adversarial RobustnessCitations: 90 Tags: adversarial-robustness, ai-safety, empirical | 2023-11-16 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E8 / R4 (95%) | 90 |
| Hijacking Large Language Models via Adversarial In-Context Learning Dongxiao Zhu, Xiangyu Zhou, Yao Qiang Published: 2023-11-16Area: Adversarial RobustnessCitations: 48 Tags: adversarial-robustness, ai-safety, empirical | 2023-11-16 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (94%) | 48 |
| JAB: Joint Adversarial Prompting and Belief Augmentation Anil Ramakrishna, Aram Galstyan, Jwala Dhamala, Kai-Wei Chang Published: 2023-11-16Area: Adversarial RobustnessCitations: 8 Tags: adversarial-robustness, ai-safety, empirical, red-teaming | 2023-11-16 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical, red-teaming | E6 / R3 (96%) | 8 |
| On the Exploitability of Reinforcement Learning with Human Feedback for Large Language Models Chaowei Xiao, Jiongxiao Wang, Junlin Wu, Muhao Chen Published: 2023-11-16Area: Alignment TrainingCitations: 32 Tags: ai-safety, alignment-training, empirical | 2023-11-16 | Alignment Training | ai-safety, alignment-training, empirical | E5 / R3 (95%) | 32 |
| Testing Language Model Agents Safely in the Wild Adam Tauman Kalai, Craig Swift, David Atkinson, David Bau Published: 2023-11-17Area: Agent SafetyCitations: 40 Tags: agent-safety, ai-safety, empirical | 2023-11-17 | Agent Safety | agent-safety, ai-safety, empirical | E4 / R2 (95%) | 40 |
| Evil Geniuses: Delving into the Safety of LLM-based Agents Hang Su, Jingyuan Zhang, Xiao Yang, Yinpeng Dong Published: 2023-11-20Area: Agent SafetyCitations: 101 Tags: adversarial-robustness, agent-safety, ai-safety, empirical | 2023-11-20 | Agent Safety | adversarial-robustness, agent-safety, ai-safety, empirical | E7 / R4 (95%) | 101 |
| GPQA: A Graduate-Level Google-Proof Q&A Benchmark Asa Cooper Stickland, Betty Li Hou, David Rein, Jackson Petty Published: 2023-11-20Area: Scalable OversightCitations: 2009 Tags: ai-safety, benchmark, scalable-oversight | 2023-11-20 | Scalable Oversight | ai-safety, benchmark, scalable-oversight | E6 / R4 (98%) | 2009 |
| Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks David Krueger, Edward Grefenstette, Ekdeep Singh Lubana, Hidenori Tanaka Published: 2023-11-21Area: Mechanistic Interp.Citations: 99 Tags: ai-safety, alignment-training, empirical, mechanistic-interp | 2023-11-21 | Mechanistic Interp. | ai-safety, alignment-training, empirical, mechanistic-interp | E5 / R3 (92%) | 99 |
| Scalable AI Safety via Doubly-Efficient Debate Geoffrey Irving, Georgios Piliouras, Jonah Brown-Cohen Published: 2023-11-23Area: Scalable OversightCitations: 40 Tags: ai-safety, scalable-oversight, theoretical | 2023-11-23 | Scalable Oversight | ai-safety, scalable-oversight, theoretical | E5 / R3 (94%) | 40 |
| Exploiting Large Language Models (LLMs) through Deception Techniques and Persuasion Principles Akbar Siami Namin, Faranak Abri, Sonali Singh Published: 2023-11-24Area: Adversarial RobustnessCitations: 29 Tags: adversarial-robustness, ai-safety, empirical | 2023-11-24 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E7 / R3 (96%) | 29 |
| Universal Jailbreak Backdoors from Poisoned Human Feedback Florian Tram猫r, Javier Rando Published: 2023-11-24Area: Adversarial RobustnessCitations: 115 Tags: adversarial-robustness, ai-safety, empirical | 2023-11-24 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (94%) | 115 |
| Localizing Lying in Llama: Understanding Instructed Dishonesty on True-False Questions Through Prompting, Probing, and Patching James Campbell, Phillip Guo, Richard Ren Published: 2023-11-25Area: Mechanistic Interp.Citations: 26 Tags: ai-safety, empirical, mechanistic-interp | 2023-11-25 | Mechanistic Interp. | ai-safety, empirical, mechanistic-interp | E5 / R3 (97%) | 26 |
| Exploring the Robustness of Model-Graded Evaluations and Automated Interpretability Ondrej Kvapil, Simon Lermen Published: 2023-11-26Area: Safety EvaluationCitations: 3 Tags: ai-safety, empirical, interpretability, safety-evaluation | 2023-11-26 | Safety Evaluation | ai-safety, empirical, interpretability, safety-evaluation | E5 / R3 (93%) | 3 |
| Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness? Dylan Hadfield-Menell, Jacob Andreas, Kevin Liu, Stephen Casper Published: 2023-11-27Area: Representation AnalysisCitations: 55 Tags: ai-safety, empirical, representation-analysis | 2023-11-27 | Representation Analysis | ai-safety, empirical, representation-analysis | E6 / R3 (95%) | 55 |
| DUnE: Dataset for Unified Editing Afra Feyza Aky眉rek, Derry Wijaya, Eric Pan, Garry Kuwanto Published: 2023-11-27Area: Model EditingCitations: 20 Tags: ai-safety, benchmark, model-editing | 2023-11-27 | Model Editing | ai-safety, benchmark, model-editing | E5 / R4 (95%) | 20 |
| How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs Bingchen Zhao, Chenhang Cui, Cihang Xie, Haoqin Tu Published: 2023-11-27Area: Multimodal SafetyCitations: 108 Tags: ai-safety, alignment-training, benchmark, multimodal-safety, safety-evaluation | 2023-11-27 | Multimodal Safety | ai-safety, alignment-training, benchmark, multimodal-safety, safety-evaluation | E5 / R3 (95%) | 108 |
| Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges Dan Qu, Hao Zhang, Heyu Chang, Nianwen Si Published: 2023-11-27Area: Model EditingCitations: 41 Tags: ai-safety, model-editing, safety-evaluation, survey | 2023-11-27 | Model Editing | ai-safety, model-editing, safety-evaluation, survey | E7 / R5 (95%) | 41 |
| Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patching Aleksandar Makelov, Georg Lange, Neel Nanda Published: 2023-11-28Area: Mechanistic Interp.Citations: 41 Tags: ai-safety, empirical, interpretability, mechanistic-interp | 2023-11-28 | Mechanistic Interp. | ai-safety, empirical, interpretability, mechanistic-interp | E6 / R3 (93%) | 41 |
| Scalable Extraction of Training Data from (Production) Language Models A. Feder Cooper, Christopher A. Choquette-Choo, Daphne Ippolito, Eric Wallace Published: 2023-11-28Area: Adversarial RobustnessCitations: 495 Tags: adversarial-robustness, ai-safety, empirical | 2023-11-28 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E7 / R4 (94%) | 495 |
| MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models Chao Yang, Jindong Gu, Xin Liu, Yichen Zhu Published: 2023-11-29Area: Multimodal SafetyCitations: 201 Tags: ai-safety, benchmark, multimodal-safety, safety-evaluation | 2023-11-29 | Multimodal Safety | ai-safety, benchmark, multimodal-safety, safety-evaluation | E5 / R3 (95%) | 201 |
| Hashmarks: Privacy-Preserving Benchmarks for High-Stakes AI Evaluation Paul Bricman Published: 2023-12-01Area: Safety EvaluationCitations: - Tags: ai-safety, safety-evaluation, theoretical | 2023-12-01 | Safety Evaluation | ai-safety, safety-evaluation, theoretical | E5 / R3 (95%) | - |
| The Philosopher's Stone: Trojaning Plugins of Large Language Models Guoxing Chen, Haojin Zhu, Minhui Xue, Rayne Holland Published: 2023-12-01Area: Adversarial RobustnessCitations: 31 Tags: adversarial-robustness, ai-safety, empirical | 2023-12-01 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (93%) | 31 |
| Eliciting Latent Knowledge from Quirky Language Models Alex Mallen, Nora Belrose Published: 2023-12-02Area: Representation AnalysisCitations: 46 Tags: ai-safety, empirical, representation-analysis | 2023-12-02 | Representation Analysis | ai-safety, empirical, representation-analysis | E5 / R3 (93%) | 46 |
| Honesty Is the Best Policy: Defining and Mitigating AI Deception Francesca Toni, Francesco Belardinelli, Francis Rhys Ward, Tom Everitt Published: 2023-12-03Area: Deception & FailureCitations: 48 Tags: ai-safety, deception-failure, theoretical | 2023-12-03 | Deception & Failure | ai-safety, deception-failure, theoretical | E4 / R2 (93%) | 48 |
| A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly Jinhao Duan, Kaidi Xu, Yifan Yao, Yuanfang Cai Published: 2023-12-04Area: Surveys & ReviewsCitations: 1022 Tags: ai-safety, survey, surveys-reviews | 2023-12-04 | Surveys & Reviews | ai-safety, survey, surveys-reviews | E6 / R3 (95%) | 1022 |