Image de couverture de livre

Know Your Limits: Entropy Estimation Modeling for Compression and Generalization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Badger, Benjamin L., Neligeorge, Matthew
Format:	Preprint
Publié:	2025
Sujets:	Computation and Language Artificial Intelligence Information Theory Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

NanoKnow: How to Know What Your Language Model Knows
par: Gu, Lingwei, et autres
Publié: (2026)

Language Modeling Is Compression
par: Delétang, Grégoire, et autres
Publié: (2023)

The Detection-Extraction Gap: Models Know the Answer Before They Can Say It
par: Wang, Hanyang, et autres
Publié: (2026)

Memorization-Compression Cycles Improve Generalization
par: Yu, Fangyuan
Publié: (2025)

Semantic Faithfulness and Entropy Production Measures to Tame Your LLM Demons and Manage Hallucinations
par: Halperin, Igor
Publié: (2025)

Language Model Memory and Memory Models for Language
par: Badger, Benjamin L.
Publié: (2026)

Learning is Forgetting: LLM Training As Lossy Compression
par: Conklin, Henry C., et autres
Publié: (2026)

Compression Represents Intelligence Linearly
par: Huang, Yuzhen, et autres
Publié: (2024)

The Stepwise Informativeness Assumption: Why are Entropy Dynamics and Reasoning Correlated in LLMs?
par: Català, Mar Gonzàlez I, et autres
Publié: (2026)

Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models
par: Badger, Benjamin L., et autres
Publié: (2026)

DIVE: Embedding Compression via Self-Limiting Gradient Updates
par: Zhao, Dongfang
Publié: (2026)

The Information of Large Language Model Geometry
par: Tan, Zhiquan, et autres
Publié: (2024)

A Survey on Large Language Models from Concept to Implementation
par: Wang, Chen, et autres
Publié: (2024)

Geometric Signatures of Compositionality Across a Language Model's Lifetime
par: Lee, Jin Hwa, et autres
Publié: (2024)

Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
par: Wei, Lai, et autres
Publié: (2024)

Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization
par: Borzechowski, Florian, et autres
Publié: (2025)

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation
par: Jung, Dongwon, et autres
Publié: (2024)

A Training-free Method for LLM Text Attribution
par: Radvand, Tara, et autres
Publié: (2025)

Measuring Uncertainty in Transformer Circuits with Effective Information Consistency
par: Krasnovsky, Anatoly A.
Publié: (2025)

SPEX: Scaling Feature Interaction Explanations for LLMs
par: Kang, Justin Singh, et autres
Publié: (2025)

Subjective Depth and Timescale Transformers: Learning Where and When to Compute
par: Wieser, Frederico, et autres
Publié: (2025)

SQuat: Subspace-orthogonal KV Cache Quantization
par: Wang, Hao, et autres
Publié: (2025)

An Information Theoretic Perspective on Agentic System Design
par: He, Shizhe, et autres
Publié: (2025)

Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain
par: Liu, Wei, et autres
Publié: (2026)

Analyzing and Improving Chain-of-Thought Monitorability Through Information Theory
par: Anwar, Usman, et autres
Publié: (2026)

Optimal Quantization for Matrix Multiplication
par: Ordentlich, Or, et autres
Publié: (2024)

A Communication-Theoretic Framework for LLM Agents: Cost-Aware Adaptive Reliability
par: Omidvar, Hamed, et autres
Publié: (2026)

How Many Features Can a Language Model Store Under the Linear Representation Hypothesis?
par: Garg, Nikhil, et autres
Publié: (2026)

Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
par: Tulchinskii, Eduard, et autres
Publié: (2023)

HeavyWater and SimplexWater: Distortion-Free LLM Watermarks for Low-Entropy Next-Token Predictions
par: Tsur, Dor, et autres
Publié: (2025)

Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
par: Nagle, Alliot, et autres
Publié: (2024)

Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds
par: Guo, Yang, et autres
Publié: (2025)

Entropy-informed Decoding: Adaptive Information-Driven Branching
par: Evans, Benjamin Patrick, et autres
Publié: (2026)

Agentic Entropy-Balanced Policy Optimization
par: Dong, Guanting, et autres
Publié: (2025)

MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
par: Tohme, Tony, et autres
Publié: (2023)

The Shape of Learning: Anisotropy and Intrinsic Dimensions in Transformer-Based Models
par: Razzhigaev, Anton, et autres
Publié: (2023)

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI
par: Garg, Sahil, et autres
Publié: (2024)

ComMer: a Framework for Compressing and Merging User Data for Personalization
par: Zeldes, Yoel, et autres
Publié: (2025)

Do Large Language Models Know How Much They Know?
par: Prato, Gabriele, et autres
Publié: (2025)

Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning
par: Corallo, Giulio, et autres
Publié: (2025)