A Survey on Model Compression for Large Language Models

Language Models are Few-Shot Learners

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Distilling the Knowledge in a Neural Network

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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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Scaling Laws for Neural Language Models

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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

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Building a Large Annotated Corpus of English: The Penn Treebank

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Neural Architecture Search with Reinforcement LearningPreprint

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Pointer Sentinel Mixture ModelsPreprint

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AQPIM: Breaking the PIM Capacity Wall for LLMs with in-Memory Activation Quantization

Matsushima, K., Okoshi, Y., Motomura, M., & Fujiki, D. (2026) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca68181.2026.11408452 · Cited by 1

Hyper-Compression: Model Compression via Hyperfunction

Fan, F., Fan, J., Wang, D., Zhang, J., Dong, Z., Zhang, S., Wang, G., & Zeng, T. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3673772 · Cited by 4

AMS-KV: Adaptive KV Caching in Multi-Scale Visual Autoregressive Transformers

Xu, B., Wang, Y., Wang, Z., & Li, P. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i32.39936 · Cited by 2

BATQuant: Outlier-Resilient MXFP4 Quantization via Learnable Block-Wise Optimization

Li, J.-F., Zhang, M., Xia, X., Bao, H., Bai, H., Dong, Z., & Yu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37010-5_37 · Cited by 6

Al Compute Architecture and Evolution Trends [Feature]

Liang, B.-S. (2026) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2025.3650725 · Cited by 3

TR-BiGRU-CRF: A Lightweight Key Information Extraction Approach for Civil Aviation Flight Crew Operational Instructions

Pan, W., Zheng, Y., Wang, Y., Chen, S., Zuo, Q., Luan, T., & Zeng, C. (2026) · Applied Sciences · doi:10.3390/app16073461 · Cited by 1

A metamorphic testing perspective on knowledge distillation for language models of code: Does the student deeply mimic the teacher?

Abdul, A. M., Rochan, M., & Roy, C. K. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112952 · Cited by 1

Balancing Accuracy and Cost: Trade‐Offs in Large Language Model Quantization for the Systems Engineering Domain

Bell, R., Madachy, R. J., & Longshore, R. (2026) · Systems Engineering · doi:10.1002/sys.70061 · Cited by 1

Adaptive LLM Inference in 6G Vehicular Networks via Layer Pruning and Offloading

Zhang, Y., Li, H., Wang, J., Luo, X., Zhu, K., & Han, Z. (2026) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc65185.2026.11555375 · Cited by 1

Small language models for on-device IoT intelligence: A survey on architectures, optimization, and deployment

Driss, M., Atitallah, S. B., Benjdira, B., AlHalawani, S., Ali, A. M., & Boulila, W. (2026) · Internet of Things · doi:10.1016/j.iot.2026.102020 · Cited by 2

Neuro-symbolic framework for multi-USV coordination: COLREGs-compliant and energy-efficient smart navigation

Huang, S., Chen, Y., Sun, M.-Y., Gao, J.-N., Zhou, Y.-H., Zeng, J. X., Wang, C.-C., Han, Y.-Z., & Xiong, Y. (2026) · Frontiers in Marine Science · doi:10.3389/fmars.2026.1865602 · Cited by 1

Cross-modal spatiotemporal prediction in transportation: A survey of modeling paradigms, representations, alignment, and reasoning

Pan, Y., Song, X., Yang, S., Wang, J., Lan, T., Li, L., Wang, D., Hu, X., Zhao, J., Zhao, J., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104617 · Cited by 1

Forecasting Individual Dairy Cow Milk Yield via Large Language Model Orchestration

Lee, S., Jung, W., Cho, S., Kim, M.-K., Park, D., WONGWANG, C., Park, H., & Park, J. (2026) · AgriEngineering · doi:10.3390/agriengineering8070295 · Cited by 1

From text to multimodality: A comprehensive survey of LLMs and MLLMs across diverse domains

Anbukkarasi, S., Hemalatha, S., Arunkumar, B., Shanmugavalli, V., Kokila, S., & A, Y. K. (2026) · ICT Express · doi:10.1016/j.icte.2026.07.023 · Cited by 1

The Versatility of Large Language Models: A Comprehensive Review and Structured Survey of Architectures, Applications, Challenges, and Future Trajectories

Peykani, P., Charles, V., Emrouznejad, A., Ghanidel, S., & Sabour, D. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10720-6 · Cited by 1

Toward sustainable on-device intelligence: A survey on energy-efficient RAG systems with small language models

Cheng, Z., Lai, L., Liu, Y., & Sun, Y. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.135448 · Cited by 1

Towards Interpreting the Behavior of Large Language Models on Software Engineering Tasks

Dipongkor, A. K. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3639478.3639798 · Cited by 1

Towards Safe, Secure, and Usable LLMs4Code

Al-Kaswan, A. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3639478.3639803 · Cited by 2

Complex-valued soft-log threshold reweighting for sparsity of complex-valued convolutional neural networks

Jiang, J., & Huang, H. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106664 · Cited by 1

Balancing Performance, Efficiency and Robustness in Open-World Machine Learning via Evolutionary Multi-objective Model Compression

Ser, J. D., Martínez-Seras, A., Bilbao, M. N., Lobo, J. L., Laña, I., & Herrera, F. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651338 · Cited by 1

Learning-Based Point Cloud Decoding with Independent and Scalable Reduced Complexity

Ghafari, M., Guarda, A. F. R., Rodrigues, N. M. M., & Pereira, F. M. B. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10648276 · Cited by 1

PrivQuant: Communication-Efficient Private Inference with Quantized Network/Protocol Co-Optimization

Xu, T., Zhong, S., Zeng, W., Wang, R., & Li, M. (2024) · doi:10.1145/3676536.3676661 · Cited by 1

Efficient One-Shot Pruning of Large Language Models with Low-Rank Approximation

Xu, Y., Cao, C., Yuan, F., Mi, R., Sun, N., Wang, D., & Liu, Y. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831406 · Cited by 1

FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking

Nanhekhan, K., Venktesh, V., Martin, E., Vatndal, H., Setty, V., & Anand, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88717-8_28 · Cited by 2

Exploring Activation Patterns of Parameters in Language Models

Wang, Y., Dai, D., Yang, Z., Ma, J., & Sui, Z. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i24.34730 · Cited by 1

Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2Preprint

Martra, P. (2025) · doi:10.36227/techrxiv.176462159.96022593/v2 · Cited by 1

The Use of Open Models in Research

Miller, K., Hoffmann, M., & Gelles, R. (2025) · doi:10.51593/20240007 · Cited by 1

An interface for clinicians: finding crucial information with language models in electronic health records

McInerney, D. J. (2024) · Digital Repository Service (Northeastern University) · doi:10.17760/d20653001

Sparsity Limit to Prune Large Language Models for on-Device AI Assistants: Llama-2 as an ExamplePreprint

Liu, B., & Xu, Y. (2024) · Preprints.org · doi:10.20944/preprints202407.1568.v1

A Survey of Theory Foundation and Key Technology in Large Models

Zhang, X., Dong, X., Wang, Y., Zhang, D., & Cao, F. (2024) · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP) · doi:10.1145/3707292.3707383

LEP: Leveraging Local Entropy Pruning for Sparsity in Large Language Models

Chen, Y., Cheng, B. C., Zhang, Y., Zhang, S., Wu, Z., & Meng, F. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888911

Beyond Scaling Laws: Towards Scientific Reasoning-Driven LLM ArchitecturesPreprint

Chen, F. (2025) · Preprints.org · doi:10.20944/preprints202504.2088.v1

Efficient Strategies for Developing Large Language Models on Low-Cost HardwarePreprint

Bhoite, H. (2025) · doi:10.36227/techrxiv.174723838.89546044/v1

In Search of a Lightweight "Good Enough" Offline Generative AI for Mobile Robots: Performance Benchmarking

Genc, Y., Altuger-Genc, G., & Tatoglu, A. (2025) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.38.1.138943

Late Breaking Results: Dynamically Scalable Pruning for Transformer-Based Large Language Models

Lee, J., Jang, S., Kim, S., Park, J.-H., Suh, I. H., Chwa, H. S., & Kim, Y. (2025) · Design, Automation, and Test in Europe · doi:10.23919/date64628.2025.10992978

On the Analysis and Distillation of Emergent Outlier Properties in Pre-trained Language Models

Zhao, T., Singh, K. Y., Appalaraju, S., Tang, P., Wu, Y. N., & Li, L. E. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.430

Exploiting Edited Large Language Models as General Scientific Optimizers

Lv, Q., Liu, T., & Wang, H. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.270 · Cited by 1

Memory Efficient LM Compression Using Fisher Information from Low-Rank Representations

Moskovskiy, D., Pletenev, S., Zagoruyko, S., & Panchenko, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97141-9_2

Examine the Opportunities and Challenges of Large Language Model (LLM) For Indic Languages

Panchal, B. Y. (2024) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v9i4s.11887

From Pixels to Shapes: Generative AI for 2D Images and 3D Models

Huo, J., Xu, S., Chen, X., Peng, W., Wang, Y., Wang, Y., & Fang, M. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9815-8_9

Ledger Lyap-LLM: Blockchain-Verified Large-Language-Model Assisted Lyapunov Routing for Energy-Harvesting Mobile Ad Hoc Networks

Haridas, S. (2025) · REST Journal on Data Analytics and Artificial Intelligence · doi:10.46632/jdaai/4/3/1

Towards compact and efficient Slovak summarization models

Petrik, S., & Nguyen, G. (2025) · Workshop on Slavic Natural Language Processing (SlavicNLP) · doi:10.18653/v1/2025.bsnlp-1.7

Technische Grundlagen der Künstlichen Intelligenz

Kather, J. N. (2025) · Springer eBooks · doi:10.1007/978-3-662-71042-5_2

Discussion on Game Changers in Science and Scientific Work Enabled Via an AI-Supported Real-Time Survey

Hrovatin, K., & Betz, U. A. K. (2025) · Springer eBooks · doi:10.1007/978-3-031-93623-4_4

Accelerating Large Language Model (LLM) Inference Through Distillation and Quantization

Bredikhin, V., Verbutska, V., & Bredikhin, D. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06832-3_2

Online Model Retraining and Instance Allocation in Edge Computing Networks

Yang, F., Ma, Q., & Gong, S. (2025) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2025-spring65109.2025.11174720

Pruning LLMs for energy saving: Is it worth the cost?

Vergallo, R., Aprile, M., Catalano, C., & Vadacca, R. (2026) · Array · doi:10.1016/j.array.2026.101420

S4T-GPTQ: A Space-for-Time Strategy For Optimizing GPTQ 4-bit Quantization

Zhang, Y., Wang, M., Peng, Y., Liu, Y., Zhou, J., Zhou, H., Zhang, S., & Cui, H. (2025) · International Conference on Machine Learning Technologies (ICMLT) · doi:10.1109/icmlt65785.2025.11193187

Designing a Smart Typo Correction and Sentiment Prediction Tool for Social Media Texts

Babu, C. V. S., Tamilvanddan, R., Kumar, S. N., & Barath, K. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-3048-8.ch007

Evaluating the Practicality of Language Models on Edge Devices for Sensor Data Analysis: A Sensible Approach?

Karthikeyan, D., Shanmugasundaram, R., Paavonen, A.-S., & Mäkitalo, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03072-6_6

FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction

Jain, A., Sharma, S., Mukherjee, K., & Pal, S. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.1197

Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions

Huang, N., Fayek, H. M., & Zhang, X. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.909 · Cited by 1

Hardware-Friendly Static Quantization Method for Video Diffusion Transformers

Yi, S., Liu, Q., & El‐Khamy, M. (2025) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr67560.2025.00042

ACoRN: Noise-Robust Abstractive Compression in Retrieval-Augmented Language Models

Kim, S., Jung, G., & Lee, S.-W. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228443 · Cited by 2

MOVie: GPU Memory Optimization for Large-Scale DNNs Training with Virtual Memory Management

Ma, Z., Tang, J., Jian, S., Zhang, J., & Tan, Y. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229114

Neural Architecture Search-Based Meta-Reinforcement Learning in Robotic Simulation Environments

Guo, J. F., Zheng, X., Chen, Y., & Zhang, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229305

HAD-MC: Domestic Edge Computing Model Compression and Deployment Based on Hardware PerceptionPreprint

Wang, J., Wang, J., Gao, J., Song, W., Kong, D., Liu, L., Li, W., & He, Z. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5770754

LLM-based Blackboard Pattern for Diagnostic SystemsPreprint

Mahr, F., Bruegge, B., Meier, S., Raum, C., Scagliola, F., Franke, J., & Ockel, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5784368

Foundation Model for Multi-Sources Hyperspectral Images Interpretation

Liu, B., Li, X., Zhang, W., & Kong, W. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11243997

Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2Preprint

Martra, P. (2025) · doi:10.36227/techrxiv.176462159.96022593/v1

Comparative Analysis of Edge vs. Cloud Contact Center Deployments: A Technical and Architectural Perspective

Kaarat, A., Batthula, V. J. R., & Segall, R. S. (2025) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce67514.2025.11257896

Quantization Methods for Energy Efficient LLM Deployments

Šubic, T. (2025) · Proceedings of the International Conference on AI Research. · doi:10.34190/icair.5.1.4367

A Multidisciplinary Bibliometric Analysis of Differences and Commonalities Between GenAI in Science

Sieciński, K., & Oliński, M. (2025) · Publications · doi:10.3390/publications13040067

DistOKD: Online Knowledge Distillation for BERT Model Compression with Source-Free Unsupervised Domain Adaptation

Ahmed, J., Sultana, S., Zhang, L., & Islam, M. S. (2025) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3766671.3766784

Low-Power Deep Learning Accelerator for Environment-Adaptive Edge AI

Suzuki, J. (2025) · Institutional Repositories DataBase (IRDB)

A Parameter-Efficient Approach to Distilling Large Language Models via Meta-learning

Cantini, R., Gabriele, N., & Orsino, A. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-05727-3_36

Mitigating the Capacity Gap in Knowledge Distillation via Iterative Tutoring

Karam, S., Aouad, R., Attieh, J., & Tekli, J. (2025) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa66935.2025.11315185

Compressing What Matters: Neuron Importance Meets Data-Aware Low Rank Approximation for Language Model Compression

Ntovas, A., Doumanoglou, A., Drakoulis, P., & Zarpalas, D. (2026) · IEEE Access · doi:10.1109/access.2026.3653132

FlipFlop: A Static Analysis-based Energy Optimization Framework for GPU Kernels

Rajput, S., Brandt, A., Elisseev, V., & Sharma, T. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3787793

Teacher-Driven Selective Fine-Tuning for LLMs

Pallati, L. B., Valenzano, R., & Melo, G. (2025) · IEEE International Conference on Collaborative Advances in Software and Computing (CASCON) · doi:10.1109/cascon66301.2025.00022

RoMeo: Mitigating Dual-dimensional Outliers with Rotated Mixed Precision Quantization

Zhang, Q., Tang, M., Zhai, M., Lei, K., & Zhai, J. (2026) · ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming · doi:10.1145/3774934.3786419

UDP: A U niversal D SP P acking Framework for Low-bitwidth MAC Acceleration on FPGAs

Wu, J., Zheng, Z., Gong, L., Wang, C., & Zhou, X. (2026) · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · doi:10.1145/3748173.3779194

MC#: Mixture Compressor for Mixture-of-Experts Large Models

Huang, W., Liao, Y., Chen, Y., Liu, J., Tan, H., Liu, S., Zhang, S., Yan, S., & Qi, X. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3664873

Stopping Criteria in Deep Neural Network Compression

Laroui, S., Seba, H., Amrouche, K., & Yahia, Y. A. A. (2025) · International Conference on Artificial Intelligence and Innovative Applications (AIIA) · doi:10.1109/aiia68273.2025.11383566

Online Model Retraining, Compression, and Instance Allocation in Edge Computing Networks

Huang, S., Yang, F., Ma, Q., & Gong, S. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3665761

Turning a Thermostat into an Agent at the Edge

Firpo, P., Berta, R., Bellotti, F., & Pau, D. P. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-17174-0_26

MoPEQ: Mixture of Mixed Precision Quantized Experts

Chitty-Venkata, K. T., Ye, J., & Emani, M. K. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00424

ThermalEdge: An Edge Computing based approach for real-time drunkenness classificationPreprint

Silva, P., Nogueira, S. L., & Felipussi, S. C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6359186

Survey of Generative Artificial Intelligence Powered Human-Data Interfaces in Big Data Systems

Gao, Y., Roy, A., Chodnekar, V., Solur, C., & Pan, G. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402173

Sig2text: A Vision‐Language Model for Non‐Cooperative Radar Signal Parsing

Yuan, J., Chen, S., Yao, S., Zhang, R., Feng, H., Jiang, K., Shang, Y., Zhao, Y., & Tang, B. (2026) · IET Radar Sonar & Navigation · doi:10.1049/rsn2.70113

Can Small Quantized VLMs Drive? An Experimental Evaluation of Small Quantized VLMs for Autonomous Driving

Mathew, S., & Gu, Z. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00440

Task-Level Energy Profiling of Large Language Model Inference

Haichun, C. H. E. N., Duan, X., Sun, Y., Wang, Y., Chen, S., Ding, Z., & Dehghanian, P. (2025) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei268505.2025.11425055

SWSC: Compressing Large Language Models via Sharing Weights for Similar Channels

Zeng, B., Tang, Y., Ji, B., Liu, X., Li, X., & Yu, J. (2025) · doi:10.1145/3788149.3788206

Beyond Bias Scores: Unmasking Vacuous Neutrality in Small Language Models

Manduru, S., & Domeniconi, C. (2026) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2026.eacl-srw.52 · Cited by 1

Optimizing Task-Specific Knowledge Distillation for Dichotomous (Yes/No) Classification

Balasubramaniam, G., Abishethvarman, V., Prasanth, S., & Kumara, B. T. G. S. (2026) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciis69028.2026.11450719

Investigating the Impact of Soft Errors in GPU-Accelerated Sparse DNNs

Ahmadilivani, M. H., Condia, J. E. R., Jenihhin, M., & Reorda, M. S. (2026) · IEEE Latin American Symposium on Circuits and Systems (LASCAS) · doi:10.1109/lascas67804.2026.11457175

SLMFL: Structural and semantic fusion for fault localization based on Small Language Models

Fan, X., Hu, J., Zheng, W., Liu, Y., & Gao, S. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108143 · Cited by 1

HybCompress-MARL: Hybrid Model Compression with Multi-agent Reinforcement Learning for CNN

Li, Q., Huang, Y., Zhang, W., Xiong, W., & Shi, N. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-21168-2_12

LLaMa-BiLoRA: Enhancing Named Entity Recognition for Resume Data Extraction Using Quantized LoRA modified LLaMA with Bidirectional Attention

Ghatas, F. S., & Abu-Elkheir, M. (2025) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet63943.2025.11472587

ILSA: Information Loss-Guided Sparsity Allocation for Pruning Large Language Models

Li, L., Wang, Y., Wang, Z., & Bao, F. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11462534

Hierarchical Sparse Plus Low Rank Compression of LLM

Kumar, P., & Gupta, A. (2025) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3799830.3799837

Structured Pruning of LLMs for Resource-Constrained Devices: A Survey

Ahmed, D., & Abbas, G. (2026) · Global Conference on Wireless and Optical Technologies (GCWOT) · doi:10.1109/gcwot69191.2026.11499662

Graph-driven knowledge stream method for knowledge management in vertical domain large language models: Case study on pit engineering application

Xia, Z., Zhong, B., Zhang, S., & Zhao, T. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104815

Energy-Efficient and Low-Carbon Model Training Through Adaptive Pruning for AI Training

Pramanik, A., Asif, M. R. I., Sharmin, S., & Nur, K. M. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11490244

AI Model Compression Methods: A Distribution-Aware Residual Entropy Quantization

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