Imagen de Portada

An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Grünefeld, Nils, Frellsen, Jes, Hardmeier, Christian
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence Computation and Language
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Tracing Uncertainty in Language Model "Reasoning"
por: Grünefeld, Nils, et al.
Publicado: (2026)

Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models
por: Cao, Qi, et al.
Publicado: (2026)

Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
por: Kruse, Maya, et al.
Publicado: (2025)

Fast Sampling for Flows and Diffusions with Lazy and Point Mass Stochastic Interpolants
por: Damsholt, Gabriel, et al.
Publicado: (2026)

Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning
por: Tsai, Yao-Hung Hubert, et al.
Publicado: (2024)

The Role of Ambiguity in Error Prediction via Uncertainty Quantification
por: Staliūnaitė, Ieva Raminta, et al.
Publicado: (2026)

The Consistency Hypothesis in Uncertainty Quantification for Large Language Models
por: Xiao, Quan, et al.
Publicado: (2025)

On Subjective Uncertainty Quantification and Calibration in Natural Language Generation
por: Wang, Ziyu, et al.
Publicado: (2024)

Adaptive Uncertainty Quantification for Generative AI
por: Kim, Jungeum, et al.
Publicado: (2024)

Anthropomimetic Uncertainty: What Verbalized Uncertainty in Language Models is Missing
por: Ulmer, Dennis, et al.
Publicado: (2025)

Multi-group Uncertainty Quantification for Long-form Text Generation
por: Liu, Terrance, et al.
Publicado: (2024)

Position: Uncertainty Quantification Needs Reassessment for Large-language Model Agents
por: Kirchhof, Michael, et al.
Publicado: (2025)

UQLM: A Python Package for Uncertainty Quantification in Large Language Models
por: Bouchard, Dylan, et al.
Publicado: (2025)

Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings
por: Grewal, Yashvir S., et al.
Publicado: (2024)

Model Merging by Uncertainty-Based Gradient Matching
por: Daheim, Nico, et al.
Publicado: (2023)

Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification
por: Bouchard, Dylan, et al.
Publicado: (2026)

IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation
por: Fan, Haozhi, et al.
Publicado: (2026)

Trustworthy Summarization via Uncertainty Quantification and Risk Awareness in Large Language Models
por: Pan, Shuaidong, et al.
Publicado: (2025)

Uncertainty Quantification for Transformer Models for Dark-Pattern Detection
por: Muñoz, Javier, et al.
Publicado: (2024)

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
por: Nikitin, Alexander, et al.
Publicado: (2024)

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
por: Fadeeva, Ekaterina, et al.
Publicado: (2024)

ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
por: Li, Mingda, et al.
Publicado: (2025)

Fine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study
por: Bouchard, Dylan, et al.
Publicado: (2026)

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
por: Duan, Jinhao, et al.
Publicado: (2023)

Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results
por: Santilli, Andrea, et al.
Publicado: (2025)

HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization
por: Zhuo, Zhijian, et al.
Publicado: (2025)

Calibration Collapse Under Sycophancy Fine-Tuning: How Reward Hacking Breaks Uncertainty Quantification in LLMs
por: Sahoo, Subramanyam
Publicado: (2026)

Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
por: Yuan, Zhihang, et al.
Publicado: (2026)

Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers
por: Bouchard, Dylan, et al.
Publicado: (2025)

SLaNC: Static LayerNorm Calibration
por: Salmani, Mahsa, et al.
Publicado: (2024)

Measuring Social Norms of Large Language Models
por: Yuan, Ye, et al.
Publicado: (2024)

Variance reduction of diffusion model's gradients with Taylor approximation-based control variate
por: Jeha, Paul, et al.
Publicado: (2024)

Navigating Uncertainty in Medical Image Segmentation
por: Zepf, Kilian, et al.
Publicado: (2024)

Where Norms and References Collide: Evaluating LLMs on Normative Reasoning
por: Abrams, Mitchell, et al.
Publicado: (2026)

SpanNorm: Reconciling Training Stability and Performance in Deep Transformers
por: Wang, Chao, et al.
Publicado: (2026)

Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks
por: Hashimoto, Wataru, et al.
Publicado: (2024)

Learn More, Forget Less: A Gradient-Aware Data Selection Approach for LLM
por: Liu, Yibai, et al.
Publicado: (2025)

Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs
por: Zhang, Yifei, et al.
Publicado: (2024)

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs
por: Wang, Zige, et al.
Publicado: (2025)

Multi-Step Alignment as Markov Games: An Optimistic Online Gradient Descent Approach with Convergence Guarantees
por: Wu, Yongtao, et al.
Publicado: (2025)