Imagen de Portada

Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Monaco, Francesco Pio, Cunegatti, Elia, Vella, Flavio, Iacca, Giovanni
Formato:	Preprint
Publicado:	2026
Materias:	Computation and Language Artificial Intelligence I.2.7
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners
por: Gao, Yifei, et al.
Publicado: (2024)

Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review
por: Peters, Sydney, et al.
Publicado: (2025)

UrduLLaMA 1.0: Dataset Curation, Preprocessing, and Evaluation in Low-Resource Settings
por: Fiaz, Layba, et al.
Publicado: (2025)

RomanLens: The Role Of Latent Romanization In Multilinguality In LLMs
por: Saji, Alan, et al.
Publicado: (2025)

Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation
por: Pator, Sanjoy
Publicado: (2026)

Bielik-Minitron-7B: Compressing Large Language Models via Structured Pruning and Knowledge Distillation for the Polish Language
por: Kinas, Remigiusz, et al.
Publicado: (2026)

$\rm SP^3$: Enhancing Structured Pruning via PCA Projection
por: Hu, Yuxuan, et al.
Publicado: (2023)

Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text
por: Oketunji, Abiodun Finbarrs
Publicado: (2023)

Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports
por: Wang, Yu, et al.
Publicado: (2026)

Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models
por: Zhang, Zhengxin, et al.
Publicado: (2024)

UniHetero: Could Generation Enhance Understanding for Vision-Language-Model at Large Data Scale?
por: Chen, Fengjiao, et al.
Publicado: (2025)

NinjaLLM: Fast, Scalable and Cost-effective RAG using Amazon SageMaker and AWS Trainium and Inferentia2
por: Xue, Tengfei, et al.
Publicado: (2024)

FastForward Pruning: Efficient LLM Pruning via Single-Step Reinforcement Learning
por: Yuan, Xin, et al.
Publicado: (2025)

QuickSilver -- Speeding up LLM Inference through Dynamic Token Halting, KV Skipping, Contextual Token Fusion, and Adaptive Matryoshka Quantization
por: Khanna, Danush, et al.
Publicado: (2025)

Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning
por: Ming, Xiaoyang, et al.
Publicado: (2026)

Robustness of Large Language Models to Perturbations in Text
por: Singh, Ayush, et al.
Publicado: (2024)

Low-Resource Court Judgment Summarization for Common Law Systems
por: Liu, Shuaiqi, et al.
Publicado: (2024)

Detecting Data Contamination in LLMs via In-Context Learning
por: Zawalski, Michał, et al.
Publicado: (2025)

Combining Data Generation and Active Learning for Low-Resource Question Answering
por: Kimmich, Maximilian, et al.
Publicado: (2022)

Large Language Model (LLM) Bias Index -- LLMBI
por: Oketunji, Abiodun Finbarrs, et al.
Publicado: (2023)

Semantic Delta: An Interpretable Signal Differentiating Human and LLMs Dialogue
por: Scantamburlo, Riccardo, et al.
Publicado: (2026)

Slim-SC: Thought Pruning for Efficient Scaling with Self-Consistency
por: Hong, Colin, et al.
Publicado: (2025)

Predictive Simultaneous Interpretation: Harnessing Large Language Models for Democratizing Real-Time Multilingual Communication
por: Iida, Kurando, et al.
Publicado: (2024)

UA-Legal-Bench: A Benchmark for Evaluating Large Language Models on Ukrainian Legal Reasoning
por: Ovcharov, Volodymyr
Publicado: (2026)

Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels
por: Dumitru, Razvan-Gabriel, et al.
Publicado: (2024)

Adversarial DPO: Harnessing Harmful Data for Reducing Toxicity with Minimal Impact on Coherence and Evasiveness in Dialogue Agents
por: Kim, San, et al.
Publicado: (2024)

Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models
por: Chang, Edward Y.
Publicado: (2024)

Extracting Small Translation Specialists from LLMs by Aggressively Pruning Experts
por: Martin, Liu O., et al.
Publicado: (2026)

The Dual-Route Model of Induction
por: Feucht, Sheridan, et al.
Publicado: (2025)

Language Models are Crossword Solvers
por: Saha, Soumadeep, et al.
Publicado: (2024)

Super Tiny Language Models
por: Hillier, Dylan, et al.
Publicado: (2024)

Adaptive Focus Memory for Language Models
por: Cruz, Christopher
Publicado: (2025)

Synergy: End-to-end Concept Model
por: Zheng, Keli, et al.
Publicado: (2025)

Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks
por: Vrettos, Konstantinos, et al.
Publicado: (2025)

Do Biased Models Have Biased Thoughts?
por: Rajwal, Swati, et al.
Publicado: (2025)

Language Model Circuits Are Sparse in the Neuron Basis
por: Arora, Aryaman, et al.
Publicado: (2026)

Inference to the Best Explanation in Large Language Models
por: Dalal, Dhairya, et al.
Publicado: (2024)

Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM
por: Codefuse, et al.
Publicado: (2025)

Entropy-Based Measurement of Value Drift and Alignment Work in Large Language Models
por: Fadli, Samih
Publicado: (2025)

Future Language Modeling from Temporal Document History
por: Li, Changmao, et al.
Publicado: (2024)