ICML 2024PastSafety & alignmentGenerative models
ICML 2024 Next Generation of AI Safety Workshop
NextGenAISafety 2024
- Submission deadline
- May 31, 2024, 12:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (93)
Fetched from OpenReview (v2) on 2026-06-10.
$\nabla \tau$: Gradient-based and Task-Agnostic Machine Unlearning
A Geometric Framework for Understanding Memorization in Generative Models
A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning
A statistical framework for weak-to-strong generalization
Accuracy on the wrong line: On the pitfalls of noisy data for OOD generalisation
AdaptiveBackdoor: Backdoored Language Model Agents that Detect Human Overseers
Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies
Adversarial Training with Synthesized Data: A Path to Robust and Generalizable Neural Networks
AI Agents with Formal Security Guarantees
AI Alignment with Changing and Influenceable Reward Functions
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing
AssistanceZero: Scalably Solving Assistance Games
Attacking Large Language Models with Projected Gradient Descent
Automatic Jailbreaking of the Text-to-Image Generative AI Systems
Automatic Pseudo-Harmful Prompt Generation for Evaluating False Refusals in Large Language Models
BELLS: A Framework Towards Future Proof Benchmarks for the Evaluation of LLM Safeguards
Bias Transmission in Large Language Models: Evidence from Gender-Occupation Bias in GPT-4
Black-Box Detection of Language Model Watermarks
Can Editing LLMs Inject Harm?
Can Go AIs be adversarially robust?
Can Language Models Safeguard Themselves, Instantly and For Free?
Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
Cascade Reward Sampling for Efficient Decoding-Time Alignment
Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification
Certifiably Robust RAG against Retrieval Corruption
Certified Robustness in NLP Under Bounded Levenshtein Distance
Chained Tuning Leads to Biased Forgetting
Consistency Checks for Language Model Forecasters
ContextCite: Attributing Model Generation to Context
CoSy: Evaluating Textual Explanations of Neurons
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors
Decomposed evaluations of geographic disparities in text-to-image models
DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
Distillation based Robustness Verification with PAC Guarantees
DiveR-CT: Diversity-enhanced Red Teaming with Relaxing Constraints
Efficient Differentially Private Fine-Tuning of Diffusion Models
Eliciting Black-Box Representations from LLMs through Self-Queries
Enhancing Concept-based Learning with Logic
Enhancing the Resilience of LLMs Against Grey-box Extractions
Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models
Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
Exploiting LLM Quantization
Exploring Scaling Trends in LLM Robustness
Fairness Through Controlled (Un)Awareness in Node Embeddings
Fairness through partial awareness: Evaluation of the addition of demographic information for bias mitigation methods
FairPFN: Transformers Can do Counterfactual Fairness
Generated Audio Detectors are Not Robust in Real-World Conditions
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
Gone With the Bits: Benchmarking Bias in Facial Phenotype Degradation Under Low-Rate Neural Compression
Hummer: Towards Limited Competitive Preference Dataset
Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses
Improving the Efficiency of Self-Supervised Adversarial Training through Latent Clustering-based Selection
In-Context Learning, Can It Break Safety?
Is ChatGPT Transforming Academics' Writing Style?
Is My Data Safe? Predicting Instance-Level Membership Inference Success for White-box and Black-box Attacks
JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
Large Language Models as Misleading Assistants in Conversation
Leveraging Multi-Color Spaces as a Defense Mechanism Against Model Inversion Attack
Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs
Manipulating Feature Visualizations with Gradient Slingshots
Marginal Fairness Sliced Wasserstein Barycenter
Measuring Goal-Directedness
Medical Unlearnable Examples: Securing Medical Data from Unauthorized Training via Sparsity-Aware Local Masking
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models
Models That Prove Their Own Correctness
Neural Interactive Proofs
On the Calibration of Conditional-Value-at-Risk
On the Robustness of Neural Networks Quantization against Data Poisoning Attacks
One-Shot Safety Alignment for Large Language Models via Optimal Dualization
Open LLMs are Necessary for Private Adaptations and Outperform their Closed Alternatives
OxonFair: A Flexible Toolkit for Algorithmic Fairness
POST: A Framework for Privacy of Soft-prompt Transfer
PrimeGuard: Safe and Helpful LLMs through Tuning-Free Routing
Privacy Auditing of Large Language Models
Private Attribute Inference from Images with Vision-Language Models
ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models
Robust Knowledge Unlearning via Mechanistic Localizations
Robustness Analysis of AI Models in Critical Energy Systems
Rule Based Rewards for Fine-Grained LLM Safety
Safer Reinforcement Learning by Going Off-policy: a Benchmark
Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs
Towards Adaptive Attacks on Constrained Tabular Machine Learning
Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques
Towards Safe Large Language Models for Medicine
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
Uncovering a Culture of AI Grassroots Experimentation by Boston City Employees: Safety Risks and Mitigation
Unfamiliar Finetuning Examples Control How Language Models Hallucinate
Using Large Language Models for Humanitarian Frontline Negotiation: Opportunities and Considerations
Weak-to-Strong Jailbreaking on Large Language Models
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models