Copertina

Estimating Semantic Alphabet Size for LLM Uncertainty Quantification

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	McCabe, Lucas H., Melamed, Rimon, Hartvigsen, Thomas, Huang, H. Howie
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Demystifying optimized prompts in language models
di: Melamed, Rimon, et al.
Pubblicazione: (2025)

Prompts have evil twins
di: Melamed, Rimon, et al.
Pubblicazione: (2023)

SENECA: Small-Sample Discrete Entropy Estimation via Self-Consistent Missing Mass
di: McCabe, Lucas H., et al.
Pubblicazione: (2026)

Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity
di: Nguyen, Dang, et al.
Pubblicazione: (2025)

DiscoUQ: Structured Disagreement Analysis for Uncertainty Quantification in LLM Agent Ensembles
di: Jiang, Bo
Pubblicazione: (2026)

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
di: Bakman, Yavuz, et al.
Pubblicazione: (2025)

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

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

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation
di: Deng, Zewei, et al.
Pubblicazione: (2026)

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

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

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

Uncertainty Quantification for In-Context Learning of Large Language Models
di: Ling, Chen, et al.
Pubblicazione: (2024)

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

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

Efficient Uncertainty Estimation for LLM-based Entity Linking in Tabular Data
di: Bono, Carlo, et al.
Pubblicazione: (2025)

The Illusion of Certainty: Uncertainty Quantification for LLMs Fails under Ambiguity
di: Tomov, Tim, et al.
Pubblicazione: (2025)

Prompt-Dependent Ranking of Large Language Models with Uncertainty Quantification
di: Menendez, Angel Rodrigo Avelar, et al.
Pubblicazione: (2026)

Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach
di: Liu, Linyu, et al.
Pubblicazione: (2024)

Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users
di: Kim, Yejin, et al.
Pubblicazione: (2024)

Constrained Discrete Diffusion
di: Cardei, Michael, et al.
Pubblicazione: (2025)

Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination
di: Agnimo, Yedidia, et al.
Pubblicazione: (2026)

Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
di: Lin, Zhen, et al.
Pubblicazione: (2023)

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph
di: Vashurin, Roman, et al.
Pubblicazione: (2024)

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
di: Ouyang, Xu, et al.
Pubblicazione: (2024)

Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume
di: Lau, Gregory Kang Ruey, et al.
Pubblicazione: (2026)

CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought
di: Zhang, Boxuan, et al.
Pubblicazione: (2025)

Recurrent Confidence Chain: Temporal-Aware Uncertainty Quantification in Large Language Models
di: Mao, Zhenjiang, et al.
Pubblicazione: (2026)

Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification
di: Liu, Shang, et al.
Pubblicazione: (2024)

Tokenisation over Bounded Alphabets is Hard
di: Kastreva, Violeta, et al.
Pubblicazione: (2025)

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

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

Uncertainty Quantification for Named Entity Recognition via Full-Sequence and Subsequence Conformal Prediction
di: Singer, Matthew, et al.
Pubblicazione: (2026)

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

Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning
di: Huang, Fanding, et al.
Pubblicazione: (2025)

An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
di: Grünefeld, Nils, et al.
Pubblicazione: (2026)

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

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

WikiBigEdit: Understanding the Limits of Lifelong Knowledge Editing in LLMs
di: Thede, Lukas, et al.
Pubblicazione: (2025)

Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems
di: Agrawal, Aakriti, et al.
Pubblicazione: (2025)