Copertina

Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Groot, Tobias, Valdenegro-Toro, Matias
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Computation and Language Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Know What You do Not Know: Verbalized Uncertainty Estimation Robustness on Corrupted Images in Vision-Language Models
di: Borszukovszki, Mirko, et al.
Pubblicazione: (2025)

Can Bayesian Neural Networks Explicitly Model Input Uncertainty?
di: Valdenegro-Toro, Matias, et al.
Pubblicazione: (2025)

Verbalized Machine Learning: Revisiting Machine Learning with Language Models
di: Xiao, Tim Z., et al.
Pubblicazione: (2024)

Uncertainty in Semantic Language Modeling with PIXELS
di: Radu, Stefania, et al.
Pubblicazione: (2025)

Uncertainty Quantification for cross-subject Motor Imagery classification
di: Manivannan, Prithviraj, et al.
Pubblicazione: (2024)

Matryoshka Query Transformer for Large Vision-Language Models
di: Hu, Wenbo, et al.
Pubblicazione: (2024)

A Survey on Hallucination in Large Vision-Language Models
di: Liu, Hanchao, et al.
Pubblicazione: (2024)

TroL: Traversal of Layers for Large Language and Vision Models
di: Lee, Byung-Kwan, et al.
Pubblicazione: (2024)

Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
di: Zhou, Yiyang, et al.
Pubblicazione: (2023)

S-GRPO: Unified Post-Training for Large Vision-Language Models
di: Yan, Yuming, et al.
Pubblicazione: (2026)

Towards Statistical Factuality Guarantee for Large Vision-Language Models
di: Li, Zhuohang, et al.
Pubblicazione: (2025)

Uncertainty-Aware Exploratory Direct Preference Optimization for Multimodal Large Language Models
di: Zhang, Huatian, et al.
Pubblicazione: (2026)

Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads
di: Cherian, Anoop, et al.
Pubblicazione: (2024)

EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models
di: Wang, Zekun, et al.
Pubblicazione: (2025)

ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time
di: Ding, Yi, et al.
Pubblicazione: (2024)

TLDR: Token-Level Detective Reward Model for Large Vision Language Models
di: Fu, Deqing, et al.
Pubblicazione: (2024)

Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations
di: Ratzlaff, Neale, et al.
Pubblicazione: (2024)

A Comprehensive Information-Decomposition Analysis of Large Vision-Language Models
di: Xiu, Lixin, et al.
Pubblicazione: (2026)

DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback
di: Chen, Yangyi, et al.
Pubblicazione: (2023)

Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models
di: Wu, Junjie, et al.
Pubblicazione: (2024)

Seeing Through Their Eyes: Evaluating Visual Perspective Taking in Vision Language Models
di: Góral, Gracjan, et al.
Pubblicazione: (2024)

Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
di: Zhou, Yiyang, et al.
Pubblicazione: (2024)

MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
di: Xia, Peng, et al.
Pubblicazione: (2024)

Progressive Multi-granular Alignments for Grounded Reasoning in Large Vision-Language Models
di: Le, Quang-Hung, et al.
Pubblicazione: (2024)

Lever LM: Configuring In-Context Sequence to Lever Large Vision Language Models
di: Yang, Xu, et al.
Pubblicazione: (2023)

The Neglected Tails in Vision-Language Models
di: Parashar, Shubham, et al.
Pubblicazione: (2024)

Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models
di: Li, Yue, et al.
Pubblicazione: (2025)

Towards Efficient Large Vision-Language Models: A Comprehensive Survey on Inference Strategies
di: Pathak, Surendra, et al.
Pubblicazione: (2026)

Evaluating and Advancing Multimodal Large Language Models in Perception Ability Lens
di: Chen, Feng, et al.
Pubblicazione: (2024)

A Vision Check-up for Language Models
di: Sharma, Pratyusha, et al.
Pubblicazione: (2024)

Calibrated Self-Rewarding Vision Language Models
di: Zhou, Yiyang, et al.
Pubblicazione: (2024)

How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey
di: Qi, Yayun, et al.
Pubblicazione: (2024)

Mostly Text, Smart Visuals: Asymmetric Text-Visual Pruning for Large Vision-Language Models
di: Li, Sijie, et al.
Pubblicazione: (2026)

Language Models as Black-Box Optimizers for Vision-Language Models
di: Liu, Shihong, et al.
Pubblicazione: (2023)

IPO: Interpretable Prompt Optimization for Vision-Language Models
di: Du, Yingjun, et al.
Pubblicazione: (2024)

Kiki or Bouba? Sound Symbolism in Vision-and-Language Models
di: Alper, Morris, et al.
Pubblicazione: (2023)

Sherlock: Self-Correcting Reasoning in Vision-Language Models
di: Ding, Yi, et al.
Pubblicazione: (2025)

Coordinated Robustness Evaluation Framework for Vision-Language Models
di: Babu, Ashwin Ramesh, et al.
Pubblicazione: (2025)

BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval
di: Miranda, Imanol, et al.
Pubblicazione: (2024)

Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
di: Ma, Yan, et al.
Pubblicazione: (2025)