ICLR 2025 Past Large language modelsDatasets
ICLR 2025 Workshop on Navigating and Addressing Data Problems for Foundation Models
ICLR 2025 Workshop Data Problems
- Submission deadline
- Feb 8, 2025, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (85)
Fetched from OpenReview (v2) on 2026-06-10.
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$f$-SCRUB: Unbounded Machine Unlearning Via $f$-divergences
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A Missing Testbed for LLM Pre-Training Membership Inference Attacks
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A Versatile Influence Function for Data Attribution with Non-Decomposable Loss
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Abg-SciQA: A dataset for Understanding and Resolving Ambiguity in Scientific Questions
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ADSO: Adaptive Data Mixture & Scale Optimization. A Multi-Scale Multi-Fidelity Bayesian Optimization Approach.
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Adversarial Attacks on Data Attribution
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Aioli: A Unified Optimization Framework for Language Model Data Mixing
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Approximations to worst-case data dropping: unmasking failure modes
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Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs
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BenchAgents: Automated Benchmark Creation with Agent Interaction
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Beyond ordinary Lipschitz constraints: Differentially Private optimization with TNC
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Blind Baselines Beat Membership Inference Attacks for Foundation Models
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Building Bridges, Not Walls: Advancing Interpretability by Unifying Feature, Data, and Model Component Attribution
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Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
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Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations
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Context-Guided Responsible Data Augmentation with Diffusion Models
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Context-Parametric Inversion: Why Instruction Finetuning Can Worsen Context Reliance
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Contrastive Private Data Synthesis via Weighted Multi-PLM Fusion
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D3: A Large Dataset for Training Code Language Models to Act Diff-by-Diff
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Data Efficient Pre-training for Language Models: An Empirical Study of Compute Efficiency and Linguistic Competence
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Data Mixing Can Induce Phase Transitions in Knowledge Acquisition
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Data-Efficient Supervised Fine-Tuning of Language Models Using Optimal Design
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Defending LVLMs Against Vision Attacks through Partial-Perception Supervision
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Demystifying Long Chain-of-Thought Reasoning in LLMs
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Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model
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Diversity Measurement and Subset Selection for Instruction Tuning Datasets
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Domain-Specific Benchmarking of Vision-Language Models: A Task Augmentation Framework Using Metadata
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DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks
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Editable Concept Bottleneck Models
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Enhancing Interpretability in Generative AI Through Search-Based Data Influence Analysis
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Enhancing Multilingual LLM Pretraining with Model-Based Data Selection
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Explaining Length Bias in LLM-Based Preference Evaluations
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From Fairness to Truthfulness: Rethinking Data Valuation Design
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Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?
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How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning
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Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
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Improving Multimodal Large Language Models in Low-Resource Language Contexts
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Information-theoretic Quantification of Inherent Discrimination Bias in Training Data for Supervised Learning
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Investigating Memorization in Video Diffusion Models
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KGGen: Text To Knowledge Graph
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Language Model Preference Evaluation with Multiple Weak Evaluators
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Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty
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LoBAM: LoRA-Based Backdoor Attack on Model Merging
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MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
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MMA: Benchmarking Multi-Modal Large Language Model in Ambiguity Contexts
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Model Collapse in the Self-Consuming Chain of Diffusion Finetuning: A Novel Perspective from Quantitative Trait Modeling
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Nepotistically Trained Generative Image Models Collapse
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NICE: Non-Differentiable Evaluation Metric-Based Data Selection for Instruction Tuning
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On the Power of Context-Enhanced Learning in LLMs
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OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning
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PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation
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PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient Conflicts
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Position: What's the next frontier for Data-centric AI? Data Savvy Agents!
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Preserving Product Fidelity in Large Scale Image Recontextualization with Diffusion Models
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Privacy Attacks on Image AutoRegressive Models
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Privacy Auditing for Large Language Models with Natural Identifiers
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Proper Dataset Valuation by Pointwise Mutual Information
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Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning
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RepFair-QGAN: Alleviating Representation Bias in Quantum Generative Adversarial Networks Using Gradient Clipping
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Revisiting Multi-Modal LLM Evaluation
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Revisiting Semi-supervised Adversarial Training via Noise-aware Online Robust Distillation
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Reward-Augmented Data Enhances Direct Preference Alignment of LLMs
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RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation
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Robust In-Context Learning via Multi-Armed Bandit-Based Partition Selection
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Rule-Based Rating and Selection of LLM Training Data
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STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings
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SubLIME*: Data Efficient Foundation Model Evaluation across Modalities, Languages and Benchmarks
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Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models
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Synthesizing Privacy-Preserving Text Data via Finetuning *without* Finetuning Billion-Scale LLMs
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Template Matters: Understanding the Role of Instruction Templates in Multimodal Language Model Evaluation and Training
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The Delta Learning Hypothesis: Preference Tuning on Weak Data Can Yield Strong Gains
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The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination
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The surprising amount of arbitrariness in Shapley-value data valuation
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TOWARD EFFICIENT INFLUENCE FUNCTION: DROPOUT AS A COMPRESSION TOOL
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Towards Comprehensive Preference Data Collection for Reward Modeling
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Towards Human-Guided, Data-Centric LLM Co-Pilots
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Towards Internet-Scale Training For Agents
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Tracing the Misuse of Personalized Textual Embeddings for Text-to-Image Models
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Training and Evaluating Language Models with Template-based Data Generation
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TsKAN: A Transparent Architecture for Improving the Interpretability of Multivariate Time Series Forecasting
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Understanding Private Learning From Feature Perspective
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Unlocking Post-hoc Dataset Inference with Synthetic Data
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Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models
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Utilizing Language Models For Synthetic Knowledge Graph Generation
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Why Does Private Fine-Tuning Resist Differential Privacy Noise? A Representation Learning Perspective