{"total":178,"rows":[{"id":"2401.15024","title":"SliceGPT: Compress Large Language Models by Deleting Rows and Columns","created":"2024-01-26","cited_by_count":385,"area":null},{"id":"2403.03853","title":"ShortGPT: Layers in Large Language Models are More Redundant Than You Expect","created":"2024-03-06","cited_by_count":383,"area":null},{"id":"2403.07378","title":"SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression","created":"2024-03-12","cited_by_count":243,"area":null},{"id":"2309.14717","title":"QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models","created":"2023-09-26","cited_by_count":155,"area":null},{"id":"2402.04291","title":"BiLLM: Pushing the Limit of Post-Training Quantization for LLMs","created":"2024-02-06","cited_by_count":144,"area":null},{"id":"2407.18921","title":"Mobile Edge Intelligence for Large Language Models: A Contemporary Survey","created":"2024-07-09","cited_by_count":128,"area":null},{"id":"2411.04330","title":"Scaling Laws for Precision","created":"2024-11-07","cited_by_count":95,"area":null},{"id":"2310.04564","title":"ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models","created":"2023-10-06","cited_by_count":94,"area":null},{"id":"2310.00034","title":"PB-LLM: Partially Binarized Large Language Models","created":"2023-09-29","cited_by_count":83,"area":null},{"id":"2402.02834","title":"Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods","created":"2024-02-05","cited_by_count":82,"area":null},{"id":"2402.11295","title":"OneBit: Towards Extremely Low-bit Large Language Models","created":"2024-02-17","cited_by_count":79,"area":null},{"id":"2405.14917","title":"SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models","created":"2024-05-23","cited_by_count":63,"area":null},{"id":"2408.09632","title":"MoDeGPT: Modular Decomposition for Large Language Model Compression","created":"2024-08-19","cited_by_count":48,"area":null},{"id":"2402.05445","title":"Accurate LoRA-Finetuning Quantization of LLMs via Information Retention","created":"2024-02-08","cited_by_count":41,"area":null},{"id":"2410.14273","title":"REEF: Representation Encoding Fingerprints for Large Language Models","created":"2024-10-18","cited_by_count":36,"area":null},{"id":"2405.01745","title":"Large Language Models for UAVs: Current State and Pathways to the Future","created":"2024-05-02","cited_by_count":33,"area":null},{"id":"2309.14021","title":"LORD: Low Rank Decomposition Of Monolingual Code LLMs For One-Shot Compression","created":"2023-09-25","cited_by_count":26,"area":null},{"id":"2409.14457","title":"Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends","created":"2024-09-22","cited_by_count":26,"area":null},{"id":"2312.00333","title":"Green Edge AI: A Contemporary Survey","created":"2023-12-01","cited_by_count":25,"area":null},{"id":"2501.18845","title":"Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities","created":"2025-01-31","cited_by_count":22,"area":null},{"id":"2402.01799","title":"Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward","created":"2024-02-02","cited_by_count":21,"area":null},{"id":"2407.12327","title":"Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale","created":"2024-07-17","cited_by_count":21,"area":null},{"id":"2501.18915","title":"Algebra Unveils Deep Learning -- An Invitation to Neuroalgebraic Geometry","created":"2025-01-31","cited_by_count":21,"area":null},{"id":"2308.14522","title":"Graph Meets LLMs: Towards Large Graph Models","created":"2023-08-28","cited_by_count":20,"area":null},{"id":"2406.16330","title":"Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging","created":"2024-06-24","cited_by_count":19,"area":null},{"id":"2407.02348","title":"Agreement-Based Cascading for Efficient Inference","created":"2024-07-02","cited_by_count":16,"area":null},{"id":"2410.04960","title":"On Efficient Variants of Segment Anything Model: A Survey","created":"2024-10-07","cited_by_count":16,"area":null},{"id":"2508.04853","title":"Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos","created":"2025-08-06","cited_by_count":16,"area":null},{"id":"2502.07184","title":"Refine Knowledge of Large Language Models via Adaptive Contrastive Learning","created":"2025-02-11","cited_by_count":15,"area":null},{"id":"2406.12311","title":"Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models","created":"2024-06-18","cited_by_count":14,"area":null},{"id":"2409.11055","title":"Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant","created":"2024-09-17","cited_by_count":14,"area":null},{"id":"2405.00314","title":"Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey","created":"2024-05-01","cited_by_count":13,"area":null},{"id":"2401.14112","title":"FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design","created":"2024-01-25","cited_by_count":12,"area":null},{"id":"2405.20835","title":"Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs","created":"2024-05-31","cited_by_count":12,"area":null},{"id":"2502.13178","title":"Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis","created":"2025-02-18","cited_by_count":12,"area":null},{"id":"2505.01658","title":"A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency","created":"2025-05-03","cited_by_count":12,"area":null},{"id":"2403.02871","title":"Quantum Mixed-State Self-Attention Network","created":"2024-03-05","cited_by_count":11,"area":null},{"id":"2406.12928","title":"Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox","created":"2024-06-15","cited_by_count":11,"area":null},{"id":"2311.09550","title":"A Speed Odyssey for Deployable Quantization of LLMs","created":"2023-11-16","cited_by_count":10,"area":null},{"id":"2402.05628","title":"RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization","created":"2024-02-08","cited_by_count":10,"area":null},{"id":"2404.15349","title":"A Survey on Multimodal Wearable Sensor-based Human Action Recognition","created":"2024-04-14","cited_by_count":10,"area":null},{"id":"2407.04581","title":"Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directions","created":"2024-07-05","cited_by_count":10,"area":null},{"id":"2505.00850","title":"ICQuant: Index Coding enables Low-bit LLM Quantization","created":"2025-05-01","cited_by_count":9,"area":null},{"id":"2505.02309","title":"Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques","created":"2025-05-05","cited_by_count":9,"area":null},{"id":"2401.01923","title":"The Internet of Things in the Era of Generative AI: Vision and Challenges","created":"2024-01-03","cited_by_count":8,"area":null},{"id":"2406.13046","title":"Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates","created":"2024-06-18","cited_by_count":8,"area":null},{"id":"2602.02848","title":"Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression","created":"2026-02-02","cited_by_count":8,"area":null},{"id":"2309.08968","title":"Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference","created":"2023-09-16","cited_by_count":7,"area":null},{"id":"2312.12472","title":"A Performance Evaluation of a Quantized Large Language Model on Various Smartphones","created":"2023-12-19","cited_by_count":7,"area":null},{"id":"2410.15116","title":"Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models","created":"2024-10-19","cited_by_count":7,"area":null},{"id":"2412.20185","title":"DecDEC: A Systems Approach to Advancing Low-Bit LLM Quantization","created":"2024-12-28","cited_by_count":7,"area":null},{"id":"2505.20112","title":"ERC-SVD: Error-Controlled SVD for Large Language Model Compression","created":"2025-05-26","cited_by_count":7,"area":null},{"id":"2404.18311","title":"Towards Incremental Learning in Large Language Models: A Critical Review","created":"2024-04-28","cited_by_count":6,"area":null},{"id":"2407.04065","title":"On the Workflows and Smells of Leaderboard Operations (LBOps): An Exploratory Study of Foundation Model Leaderboards","created":"2024-07-04","cited_by_count":6,"area":null},{"id":"2408.14398","title":"On the Limitations of Language Targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning","created":"2024-08-26","cited_by_count":6,"area":null},{"id":"2410.09012","title":"Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models","created":"2024-10-11","cited_by_count":6,"area":null},{"id":"2502.03460","title":"Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training","created":"2025-02-05","cited_by_count":6,"area":null},{"id":"2510.08757","title":"LOTION: Smoothing the Optimization Landscape for Quantized Training","created":"2025-10-09","cited_by_count":6,"area":null},{"id":"2603.16590","title":"BATQuant: Outlier-resilient MXFP4 Quantization via Learnable Block-wise Optimization","created":"2026-03-17","cited_by_count":6,"area":null},{"id":"2312.14647","title":"Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities","created":"2023-12-22","cited_by_count":5,"area":null},{"id":"2403.08077","title":"A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection","created":"2024-03-12","cited_by_count":5,"area":null},{"id":"2405.15842","title":"Model Cascading for Code: A Cascaded Black-Box Multi-Model Framework for Cost-Efficient Code Completion with Self-Testing","created":"2024-05-24","cited_by_count":5,"area":null},{"id":"2504.21174","title":"Efficient LLMs with AMP: Attention Heads and MLP Pruning","created":"2025-04-29","cited_by_count":5,"area":null},{"id":"2505.22922","title":"Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking","created":"2025-05-28","cited_by_count":5,"area":null},{"id":"2507.07580","title":"COALA: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation","created":"2025-07-10","cited_by_count":5,"area":null},{"id":"2510.12077","title":"Compressibility Measures Complexity: Minimum Description Length Meets Singular Learning Theory","created":"2025-10-14","cited_by_count":5,"area":null},{"id":"2510.16805","title":"Mixed-Precision Quantization for Language Models: Techniques and Prospects","created":"2025-10-19","cited_by_count":5,"area":null},{"id":"2309.15857","title":"A Survey on Image-text Multimodal Models","created":"2023-09-23","cited_by_count":4,"area":null},{"id":"2405.07638","title":"DoLLM: How Large Language Models Understanding Network Flow Data to Detect Carpet Bombing DDoS","created":"2024-05-13","cited_by_count":4,"area":null},{"id":"2407.04711","title":"MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models","created":"2024-05-14","cited_by_count":4,"area":null},{"id":"2501.19090","title":"Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models","created":"2025-01-31","cited_by_count":4,"area":null},{"id":"2505.20132","title":"Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks","created":"2025-05-26","cited_by_count":4,"area":null},{"id":"2510.02823","title":"The Curious Case of In-Training Compression of State Space Models","created":"2025-10-03","cited_by_count":4,"area":null},{"id":"2511.22334","title":"Edge Deployment of Small Language Models, a comprehensive comparison of CPU, GPU and NPU backends","created":"2025-11-27","cited_by_count":4,"area":null},{"id":"2603.03597","title":"NuMuon: Nuclear-Norm-Constrained Muon for Compressible LLM Training","created":"2026-03-04","cited_by_count":4,"area":null},{"id":"2312.16351","title":"LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processing","created":"2023-12-26","cited_by_count":3,"area":null},{"id":"2405.17799","title":"Exploring Activation Patterns of Parameters in Language Models","created":"2024-05-28","cited_by_count":3,"area":null},{"id":"2409.00592","title":"Hyper-Compression: Model Compression via Hyperfunction","created":"2024-09-01","cited_by_count":3,"area":null},{"id":"2410.05911","title":"Accelerating Error Correction Code Transformers","created":"2024-10-08","cited_by_count":3,"area":null},{"id":"2410.14766","title":"Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks","created":"2024-10-18","cited_by_count":3,"area":null},{"id":"2411.17309","title":"PIM-AI: A Novel Architecture for High-Efficiency LLM Inference","created":"2024-11-26","cited_by_count":3,"area":null},{"id":"2501.17905","title":"DReSS: Data-driven Regularized Structured Streamlining for Large Language Models","created":"2025-01-29","cited_by_count":3,"area":null},{"id":"2501.18980","title":"Symmetric Pruning of Large Language Models","created":"2025-01-31","cited_by_count":3,"area":null},{"id":"2502.01717","title":"Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation","created":"2025-02-03","cited_by_count":3,"area":null},{"id":"2502.02766","title":"Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks","created":"2025-02-04","cited_by_count":3,"area":null},{"id":"2503.09114","title":"Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge","created":"2025-03-12","cited_by_count":3,"area":null},{"id":"2503.22064","title":"Multi-Task Semantic Communications via Large Models","created":"2025-03-28","cited_by_count":3,"area":null},{"id":"2504.13932","title":"Enhancing Ultra-Low-Bit Quantization of Large Language Models Through Saliency-Aware Partial Retraining","created":"2025-04-14","cited_by_count":3,"area":null},{"id":"2504.16394","title":"ConTextual: Improving Clinical Text Summarization in LLMs with Context-preserving Token Filtering and Knowledge Graphs","created":"2025-04-23","cited_by_count":3,"area":null},{"id":"2505.06907","title":"A Survey on Foundation Models for Personalized Federated Intelligence","created":"2025-05-11","cited_by_count":3,"area":null},{"id":"2506.10205","title":"AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent","created":"2025-06-11","cited_by_count":3,"area":null},{"id":"2506.11040","title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2507.16872","title":"CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage","created":"2025-07-22","cited_by_count":3,"area":null},{"id":"2510.22101","title":"Scaling Up Efficient Small Language Models Serving and Deployment for Semantic Job Search","created":"2025-10-25","cited_by_count":3,"area":null},{"id":"2511.06516","title":"You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations","created":"2025-11-09","cited_by_count":3,"area":null},{"id":"2603.18426","title":"Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression","created":"2026-03-19","cited_by_count":3,"area":null},{"id":"2403.06497","title":"QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning","created":"2024-03-11","cited_by_count":2,"area":null},{"id":"2411.06581","title":"HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization","created":"2024-11-10","cited_by_count":2,"area":null},{"id":"2505.06454","title":"Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning","created":"2025-05-09","cited_by_count":2,"area":null},{"id":"2505.21171","title":"M-Wanda: Improving One-Shot Pruning for Multilingual LLMs","created":"2025-05-27","cited_by_count":2,"area":null}],"next_cursor":"100"}