ICLR 2024PastLarge language modelsFairness & ethics
ICLR 2024 Workshop on Reliable and Responsible Foundation Models
ICLR 2024 R2-FM Workshop
- Submission deadline
- Feb 11, 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 (66)
Fetched from OpenReview (v2) on 2026-06-10.
©Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model
A StrongREJECT for Empty Jailbreaks
Actions Speak Louder than Words: Superficial Fairness Alignment in LLMs
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
AI-Generated Images Introduce Invisible Relevance Bias to Text-Image Retrieval
Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
Augmentation Alone Leads to Generalization
AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition
Boosting Jailbreak Attack With Momentum
Can Generative Multimodal Models Count to Ten?
Can Large Language Models Achieve Calibration with In-Context Learning?
Can Large Language Models Reason Robustly with Noisy Rationales?
Chain-of-Verification Reduces Hallucination in Large Language Models
Composing Knowledge and Compression Interventions for Language Models
Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation
Dataset MKSL for measuring adequate response performance by knowledge level
Does Data Contamination Make a Difference? Insights from Intentionally Contaminating Pre-training Data For Language Models
Evaluating Model Bias Requires Characterizing its Mistakes
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
Explaining latent representations of generative models with large multimodal models
Explicit Knowledge Factorization Meets In-Context Learning: What Do We Gain?
Exploring the Robustness of In-Context Learning with Noisy Labels
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
Hijacking Context in Large Multi-modal Models
How many Opinions does your LLM have? Improving Uncertainty Estimation in NLG
How to train your VIT for OOD detection
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
Instruction Tuning for Secure Code Generation
Keeping LLMs Aligned After Fine-tuning: The Crucial Role of Prompt Templates
LARGE LANGUAGE MODEL CASCADES WITH MIXTURE OF THOUGHT REPRESENTATIONS FOR COST-EFFICIENT REASONING
Large Language Models are Anonymizers
Mapping Social Choice Theory to RLHF
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks
Memorization and Privacy Risks in Domain-Specific Large Language Models
On Fairness Implications and Evaluations of Low-Rank Adaptation of Large Models
Personalized Language Modeling from Personalized Human Feedback
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
Preventing Memorized Completions through White-Box Filtering
Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
Prompting for Robustness: Extracting Robust Classifiers from Foundation Models
ProTransformer: Robustify Transformers via Plug-and-Play Paradigm
Questioning the Survey Responses of Large Language Models
RAMBLA: A FRAMEWORK FOR EVALUATING THE RELIABILITY OF LLMS AS ASSISTANTS IN THE BIOMEDICAL DOMAIN
Re-Ex: Revising after Explanation reduces the Factual Errors in LLM Responses
Robust CLIP: Unsupervised Adversarial Fine-tuning of Vision Embeddings for Robust Large Vision-Language Models
Scaling Compute Is Not All You Need for Adversarial Robustness
Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
Self-Alignment of Large Language Models via Social Scene Simulation
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation
Setting the Record Straight on Transformer Oversmoothing
Skip $\textbackslash n$: A simple method to reduce hallucination in Large Vision-Language Models
Steering LLMs Towards Unbiased Responses: A Causality-Guided Debiasing Framework
texplain: Post-hoc Textual Explanation of Image Classifiers with Pre-trained Language Models
THE BIAS OF HARMFUL LABEL ASSOCIATIONS IN VISION-LANGUAGE MODELS
Towards Logically Consistent Language Models via Probabilistic Reasoning
Towards Personalized AI: Early-stopping Low-Rank Adaptation of Foundation Models
Unified Hallucination Detection for Multimodal Large Language Models
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation
Unsolvable Problem Detection for Vision Language Models
Value Augmented Sampling: Predict Your Rewards To Align Language Models
Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
Watermark Stealing in Large Language Models
WAVES: Benchmarking the Robustness of Image Watermarks
WorldBench: Quantifying Geographic Disparities in LLM Factual Recall