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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 |
|---|---|---|---|---|---|
| Jatmo: Prompt Injection Defense by Task-Specific Finetuning Basel Alomair, Chawin Sitawarin, David Wagner, Elizabeth Sun Published: 2023-12-29Area: Adversarial RobustnessCitations: 102 Tags: adversarial-robustness, ai-safety, empirical | 2023-12-29 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (95%) | 102 |
| Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles Bo Ding, Dawei Feng, Han Zhang, Huaimin Wang Published: 2023-12-30Area: Alignment TrainingCitations: 43 Tags: ai-safety, alignment-training, empirical | 2023-12-30 | Alignment Training | ai-safety, alignment-training, empirical | E5 / R3 (96%) | 43 |
| A Comprehensive Study of Knowledge Editing for Large Language Models Bozhong Tian, Fei Huang, Huajun Chen, Jia-Chen Gu Published: 2024-01-02Area: Model EditingCitations: 134 Tags: ai-safety, model-editing, survey | 2024-01-02 | Model Editing | ai-safety, model-editing, survey | E5 / R3 (95%) | 134 |
| A Mechanistic Understanding of Alignment Algorithms: A Case Study on DPO and Toxicity Andrew Lee, Itamar Pres, Jonathan K. Kummerfeld, Martin Wattenberg Published: 2024-01-03Area: Mechanistic Interp.Citations: 165 Tags: ai-safety, alignment-training, empirical, mechanistic-interp | 2024-01-03 | Mechanistic Interp. | ai-safety, alignment-training, empirical, mechanistic-interp | E5 / R3 (94%) | 165 |
| Quantifying stability of non-power-seeking in artificial agents Evan Ryan Gunter, Victoria Krakovna, Yevgeny Liokumovich Published: 2024-01-07Area: Formal/TheoreticalCitations: 2 Tags: ai-safety, formaltheoretical, theoretical | 2024-01-07 | Formal/Theoretical | ai-safety, formaltheoretical, theoretical | E5 / R3 (94%) | 2 |
| Evaluating Brain-Inspired Modular Training in Automated Circuit Discovery for Mechanistic Interpretability Jatin Nainani Published: 2024-01-08Area: Mechanistic Interp.Citations: 3 Tags: ai-safety, empirical, interpretability, mechanistic-interp | 2024-01-08 | Mechanistic Interp. | ai-safety, empirical, interpretability, mechanistic-interp | E5 / R3 (95%) | 3 |
| Agent Alignment in Evolving Social Norms Qinyuan Cheng, Shimin Li, Tianxiang Sun, Xipeng Qiu Published: 2024-01-09Area: Agent SafetyCitations: 12 Tags: agent-safety, ai-safety, alignment-training, empirical | 2024-01-09 | Agent Safety | agent-safety, ai-safety, alignment-training, empirical | E5 / R3 (96%) | 12 |
| Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Buck Shlegeris Published: 2024-01-10Area: Deception & FailureCitations: 303 Tags: adversarial-robustness, ai-safety, deception-failure, empirical | 2024-01-10 | Deception & Failure | adversarial-robustness, ai-safety, deception-failure, empirical | E8 / R3 (97%) | 303 |
| EraseDiff: Erasing Data Influence in Diffusion Models Jing Wu, Mehrtash Harandi, Munawar Hayat, Trung Le Published: 2024-01-11Area: Model EditingCitations: 28 Tags: ai-safety, empirical, model-editing | 2024-01-11 | Model Editing | ai-safety, empirical, model-editing | E5 / R3 (94%) | 28 |
| Manipulating Feature Visualizations with Gradient Slingshots Alexander Warnecke, Dilyara Bareeva, Kirill Bykov, Klaus-Robert M眉ller Published: 2024-01-11Area: Adversarial RobustnessCitations: 6 Tags: adversarial-robustness, ai-safety, empirical | 2024-01-11 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (95%) | 6 |
| Patchscopes: A Unifying Framework for Inspecting Hidden Representations of Language Models Adam Pearce, Asma Ghandeharioun, Avi Caciularu, Lucas Dixon Published: 2024-01-11Area: Representation AnalysisCitations: 174 Tags: ai-safety, empirical, interpretability, representation-analysis | 2024-01-11 | Representation Analysis | ai-safety, empirical, interpretability, representation-analysis | E5 / R3 (95%) | 174 |
| Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems Chuanpu Fu, Junwu Xiong, Ke Xu, Peiyang Li Published: 2024-01-11Area: Surveys & ReviewsCitations: 104 Tags: ai-safety, survey, surveys-reviews | 2024-01-11 | Surveys & Reviews | ai-safety, survey, surveys-reviews | E7 / R5 (97%) | 104 |
| Secrets of RLHF in Large Language Models Part II: Reward Modeling Binghai Wang, Caishuang Huang, Chenyu Shi, Enyu Zhou Published: 2024-01-11Area: Alignment TrainingCitations: 148 Tags: ai-safety, alignment-training, empirical | 2024-01-11 | Alignment Training | ai-safety, alignment-training, empirical | E6 / R4 (94%) | 148 |
| TOFU: A Task of Fictitious Unlearning for LLMs Avi Schwarzschild, J. Zico Kolter, Pratyush Maini, Zachary C. Lipton Published: 2024-01-11Area: Safety EvaluationCitations: 351 Tags: ai-safety, benchmark, safety-evaluation | 2024-01-11 | Safety Evaluation | ai-safety, benchmark, safety-evaluation | E4 / R3 (95%) | 351 |
| Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning Fengjun Pan, Jinming Wen, Luu Anh Tuan, Meihuizi Jia Published: 2024-01-11Area: Adversarial RobustnessCitations: 73 Tags: adversarial-robustness, ai-safety, empirical | 2024-01-11 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E4 / R3 (97%) | 73 |
| How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs Diyi Yang, Hongpeng Lin, Jingwen Zhang, Ruoxi Jia Published: 2024-01-12Area: Adversarial RobustnessCitations: 529 Tags: adversarial-robustness, ai-safety, empirical | 2024-01-12 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E6 / R4 (97%) | 529 |
| Intention Analysis Makes LLMs A Good Jailbreak Defender Dacheng Tao, Lefei Zhang, Liang Ding, Yuqi Zhang Published: 2024-01-12Area: Adversarial RobustnessCitations: 63 Tags: adversarial-robustness, ai-safety, empirical | 2024-01-12 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (96%) | 63 |
| Revisiting Jailbreaking for Large Language Models: A Representation Engineering Perspective Changze Lv, Muling Wu, Shihan Dou, Tianlong Li Published: 2024-01-12Area: Adversarial RobustnessCitations: 34 Tags: adversarial-robustness, ai-safety, empirical | 2024-01-12 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (94%) | 34 |
| The Unreasonable Effectiveness of Easy Training Data for Hard Tasks Mohit Bansal, Peter Clark, Peter Hase, Sarah Wiegreffe Published: 2024-01-12Area: Scalable OversightCitations: 47 Tags: ai-safety, empirical, scalable-oversight | 2024-01-12 | Scalable Oversight | ai-safety, empirical, scalable-oversight | E5 / R3 (95%) | 47 |
| AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models Beichen Wang, Chong Zhang, Dong Shu, Mingyu Jin Published: 2024-01-17Area: Adversarial RobustnessCitations: 27 Tags: adversarial-robustness, ai-safety, benchmark, safety-evaluation | 2024-01-17 | Adversarial Robustness | adversarial-robustness, ai-safety, benchmark, safety-evaluation | E5 / R3 (93%) | 27 |
| R-Judge: Benchmarking Safety Risk Awareness for LLM Agents Binglin Zhou, Fangqi Li, Gongshen Liu, Lingzhong Dong Published: 2024-01-18Area: Agent SafetyCitations: 156 Tags: agent-safety, ai-safety, benchmark | 2024-01-18 | Agent Safety | agent-safety, ai-safety, benchmark | E5 / R3 (96%) | 156 |
| Self-Rewarding Language Models Jason Weston, Jing Xu, Kyunghyun Cho, Richard Yuanzhe Pang Published: 2024-01-18Area: Alignment TrainingCitations: 504 Tags: ai-safety, alignment-training, empirical | 2024-01-18 | Alignment Training | ai-safety, alignment-training, empirical | E5 / R3 (96%) | 504 |
| BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models Bhaskar Ramasubramanian, Bo Li, Fengqing Jiang, Radha Poovendran Published: 2024-01-20Area: Adversarial RobustnessCitations: 85 Tags: adversarial-robustness, ai-safety, empirical | 2024-01-20 | Adversarial Robustness | adversarial-robustness, ai-safety, empirical | E5 / R3 (98%) | 85 |
| Deception and Manipulation in Generative AI Christian Tarsney Published: 2024-01-20Area: Deception & FailureCitations: 21 Tags: ai-safety, deception-failure, theoretical | 2024-01-20 | Deception & Failure | ai-safety, deception-failure, theoretical | E6 / R3 (93%) | 21 |
| InferAligner: Inference-Time Alignment for Harmlessness through Cross-Model Guidance Botian Jiang, Chenkun Tan, Dong Zhang, Ke Ren Published: 2024-01-20Area: Model EditingCitations: 78 Tags: ai-safety, alignment-training, empirical, model-editing | 2024-01-20 | Model Editing | ai-safety, alignment-training, empirical, model-editing | E5 / R3 (95%) | 78 |
| Linear Alignment: A Closed-form Solution for Aligning Human Preferences without Tuning and Feedback Dahua Lin, Hang Yan, Junjie Ye, Qiming Ge Published: 2024-01-21Area: Alignment TrainingCitations: 22 Tags: ai-safety, alignment-training, theoretical | 2024-01-21 | Alignment Training | ai-safety, alignment-training, theoretical | E5 / R4 (96%) | 22 |
| PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety Feng Zhao, Hongzhi Gao, Huchuan Lu, Jing Shao Published: 2024-01-22Area: Agent SafetyCitations: 75 Tags: agent-safety, ai-safety, empirical, safety-evaluation | 2024-01-22 | Agent Safety | agent-safety, ai-safety, empirical, safety-evaluation | E6 / R4 (95%) | 75 |
| Universal Neurons in GPT2 Language Models Dimitris Bertsimas, Neel Nanda, Qinyi Sun, Tara Rezaei Kheirkhah Published: 2024-01-22Area: Mechanistic Interp.Citations: 83 Tags: ai-safety, empirical, mechanistic-interp | 2024-01-22 | Mechanistic Interp. | ai-safety, empirical, mechanistic-interp | E5 / R3 (93%) | 83 |
| WARM: On the Benefits of Weight Averaged Reward Models Alexandre Ram茅, Geoffrey Cideron, Johan Ferret, L茅onard Hussenot Published: 2024-01-22Area: Alignment TrainingCitations: 134 Tags: ai-safety, alignment-training, empirical | 2024-01-22 | Alignment Training | ai-safety, alignment-training, empirical | E5 / R3 (94%) | 134 |
| A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments Aryaman Arora, Atticus Geiger, Christopher Potts, Jing Huang Published: 2024-01-23Area: Mechanistic Interp.Citations: 9 Tags: ai-safety, empirical, interpretability, mechanistic-interp | 2024-01-23 | Mechanistic Interp. | ai-safety, empirical, interpretability, mechanistic-interp | E5 / R3 (94%) | 9 |