ICLR 2025 Past Large language models
ICLR Workshop: Quantify Uncertainty and Hallucination in Foundation Models: The Next Frontier in Reliable AI
QUESTION
- Submission deadline
- Feb 6, 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 (37)
Fetched from OpenReview (v2) on 2026-06-10.
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[TINY] Building Bridges of Thought: Using the Power of Association to Inspire Creativity in Large Language Models
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[TINY] Vision language models can implicitly quantify aleatoric uncertainty
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Adaptive Elicitation of Latent Information Using Natural Language
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Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
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Assessing Confidence in Large Language Models by Classifying Task Correctness using Similarity Features
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Can Your Uncertainty Scores Detect Hallucinated Entity?
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Conformal Structured Prediction
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Detecting Unreliable Responses in Generative Vision-Language Models via Visual Uncertainty
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FastRM: An efficient and automatic explainability framework for multimodal generative models
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Finetuning Language Models to Emit Linguistic Expressions of Uncertainty
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Generative Uncertainty in Diffusion Models
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How to Steer LLM Latents for Hallucination Detection?
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Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
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Learning on LLM Output Signatures for Gray Box LLM Behavior Analysis
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LongProLIP: A Probabilistic Vision-Language Model with Long Context Text
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Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods
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On Verbalized Confidence Scores for LLMs
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Predictive Inference Is Really Free with In-Context Learning
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Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction
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Rethinking Uncertainty Estimation in Natural Language Generation
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Sample-Focused Approach for Robust Uncertainty Quantification in LLMs
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Scalable Thompson Sampling via Ensemble++
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Semantic-Level Confidence Calibration of Language Models via Temperature Scaling
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TINY: Rethinking Selection Bias in LLMs: Quantification and Mitigation using Efficient Majority Voting
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TINY: Semantic-based Uncertainty Quantification in LLMS: A Case Study on Medical Explanation Generation Task.
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To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic Retrieval Augmented Generation
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Toward Trustworthy Neural Program Synthesis
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Towards Lighter and Robust Evaluation for Retrieval Augmented Generation
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Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
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Uncertainty of Vision Medical Foundation Models
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Uncertainty Quantification for MLLMs
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Uncertainty quantification in fine-tuned LLMs using LoRA ensembles
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Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models
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Uncertainty-Aware Step-wise Verification with Generative Reward Models
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Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach
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Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models
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Understanding the Sources of Uncertainty for Large Language and Multimodal Models