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Efficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review

https://doi.org/10.1145/3728636
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11263-021-01453-zopenalex-snapshot
Knowledge Distillation: A Survey
inferredcites →https://doi.org/10.1016/j.aiopen.2023.08.012openalex-snapshot
GPT understands, too
inferredcites →https://doi.org/10.1016/j.infsof.2008.09.009openalex-snapshot
Systematic literature reviews in software engineering – A systematic literature review
inferredcites →https://doi.org/10.1016/s0079-7421(08)60536-8openalex-snapshot
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
inferredcites →https://doi.org/10.1093/jamia/ocae045openalex-snapshot
PMC-LLaMA: toward building open-source language models for medicine
inferredcites →https://doi.org/10.1109/sc41404.2022openalex-snapshot
SC22: International Conference for High Performance Computing, Networking, Storage and Analysis
inferredcites →https://doi.org/10.1109/tvlsi.2023.3282046openalex-snapshot
X-Former: In-Memory Acceleration of Transformers
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/139669openalex-snapshot
Proceedings of the 19th annual international symposium on Computer architecture - ISCA '92
inferredcites →https://doi.org/10.1162/tacl_a_00704openalex-snapshot
A Survey on Model Compression for Large Language Models
inferredcites →https://doi.org/10.1201/9781003162810-13openalex-snapshot
A Survey of Quantization Methods for Efficient Neural Network Inference
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-mainopenalex-snapshot
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
inferredcites →https://doi.org/10.18653/v1/2021.acl-long.353openalex-snapshot
Prefix-Tuning: Optimizing Continuous Prompts for Generation
inferredcites →https://doi.org/10.18653/v1/2021.bionlp-1openalex-snapshot
Proceedings of the 20th Workshop on Biomedical Language Processing
inferredcites →https://doi.org/10.18653/v1/2021.findings-aclopenalex-snapshot
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
inferredcites →https://doi.org/10.18653/v1/2021.naacl-mainopenalex-snapshot
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
inferredcites →https://doi.org/10.18653/v1/2021.naacl-main.410openalex-snapshot
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
inferredcites →https://doi.org/10.18653/v1/2022.acl-shortopenalex-snapshot
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
inferredcites →https://doi.org/10.18653/v1/2022.findings-aclopenalex-snapshot
Findings of the Association for Computational Linguistics: ACL 2022
inferredcites →https://doi.org/10.18653/v1/2023.emnlp-mainopenalex-snapshot
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
inferredcites →https://doi.org/10.18653/v1/2023.findings-emnlpopenalex-snapshot
Findings of the Association for Computational Linguistics: EMNLP 2023
inferredcites →https://doi.org/10.18653/v1/2024.acl-longopenalex-snapshot
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
inferredcites →https://doi.org/10.18653/v1/2024.acl-long.805openalex-snapshot
MultiLegalPile: A 689GB Multilingual Legal Corpus
inferredcites →https://doi.org/10.18653/v1/2024.findings-naaclopenalex-snapshot
Findings of the Association for Computational Linguistics: NAACL 2024
inferredcites →https://doi.org/10.18653/v1/d18-1openalex-snapshot
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
inferredcites →https://doi.org/10.18653/v1/d19-1openalex-snapshot
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
inferredcites →https://doi.org/10.3115/1075218openalex-snapshot
Proceedings of the 38th Annual Meeting on Association for Computational Linguistics - ACL '00
inferredcites →https://doi.org/10.3115/1613715openalex-snapshot
Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08
inferredcites →https://doi.org/10.3390/app11146421openalex-snapshot
What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams
inferredcites →https://doi.org/10.4135/9781483368924.n25openalex-snapshot
Amsterdam, The Netherlands
inferredcites →https://doi.org/10.48550/arxiv.1710.09412openalex-snapshot
mixup: Beyond Empirical Risk Minimization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-23607-4_19openalex-snapshot
The Next Stage of Evolutionary Computation in the Era of Agentic Generative AI
inferred← citeshttps://doi.org/10.1007/s10994-026-07142-7openalex-snapshot
Federated Learning for Large Language Models: Opportunities, Challenges, and Open Research Directions
inferred← citeshttps://doi.org/10.1016/j.procs.2025.09.405openalex-snapshot
Java Source Code Vulnerability Detection Using Large Language Model
inferred← citeshttps://doi.org/10.1109/cvprf71909.2026.00239openalex-snapshot
TransKV: A Data-Driven Pruning Method for Large Foundation Models
inferred← citeshttps://doi.org/10.1109/iceei68459.2025.11331078openalex-snapshot
BERT with Knowledge Distillation for Low-Latency Multilingual Chatbots in Smart Cities
inferred← citeshttps://doi.org/10.1109/icicc71012.2026.11637613openalex-snapshot
Communication-Efficient Distributed Training Framework for Large Language Models Based on Adaptive Parameter Synchronization
inferred← citeshttps://doi.org/10.1109/ies67184.2025.11161662openalex-snapshot
The Survey of Model Scale Impact on Knowledge Distillation Efficiency in LLM
inferred← citeshttps://doi.org/10.1109/mcom.001.2500472openalex-snapshot
Prompt-Driven Low-Altitude Edge Intelligence: Modular Agents and Generative Reasoning
inferred← citeshttps://doi.org/10.1109/tai.2026.3656155openalex-snapshot
Deployment-Aware Compression of Large Language Models for Edge Intelligence: A System-Level Survey
inferred← citeshttps://doi.org/10.1109/tnse.2025.3597161openalex-snapshot
Split Learning Based Cloud-Edge-End Collaborative Model Training in Heterogeneous Networks
inferred← citeshttps://doi.org/10.1145/3746252.3760795openalex-snapshot
Quantum-Amplitude Embedded Adaptation for Parameter-Efficient Fine-Tuning in Large Language Models
inferred← citeshttps://doi.org/10.1149/1945-7111/ae9406openalex-snapshot
From Task-Specific AI to Battery Foundation Models
inferred← citeshttps://doi.org/10.3390/electronics15194473openalex-snapshot
A Survey on Energy-Efficiency Mechanisms for Large Language Model Training: Measurement, Optimization Mechanisms, Evidence Boundaries, and Future Research Directions
inferred← citeshttps://doi.org/10.3390/s26165133openalex-snapshot
An LLM-Based Framework for the Automatic Generation of SysML Models
inferred← citeshttps://doi.org/10.3934/aci.2026009openalex-snapshot
From carbon-intensive to carbon-neutral: sustainability and energy efficiency in small language models: bibliometric analysis (2020–2026)
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