mlsanityData to 7 Oct 2026

A Survey on Collaborating Small and Large Language Models for Performance, Cost-effectiveness, Cloud-edge Privacy, and Trustworthiness

arXiv 2510.13890 ↗First posted 14 Oct 2025cs.CL cs.AIcited by 5

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Abstract (arXiv)

Large language models (LLMs) have achieved remarkable progress across domains and applications but face challenges such as high fine-tuning costs, inference latency, limited edge deployability, and reliability concerns. Small language models (SLMs), with compact, efficient, and adaptable features, offer promising solutions. Building on this potential, recent research explores collaborative frameworks that integrate their complementary strengths, leveraging SLMs' specialization and efficiency with LLMs' generalization and reasoning to address diverse objectives across tasks and deployment scenarios. Motivated by these developments, this paper presents a systematic survey of SLM-LLM collaboration from the perspective of collaboration objectives. We propose a taxonomy covering four goals: performance enhancement, cost-effectiveness, cloud-edge privacy, and trustworthiness. Under this framework, we review representative methods, summarize design paradigms, and outline open challenges and future directions toward efficient and secure SLM-LLM collaboration. The collected papers are available at https://github.com/FairyFali/SLMs-Survey.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 112 of its 161 references that are in the map. First posted on arXiv 14 Oct 2025.

Builds on

112 of 161 references.

References · 112

  1. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  3. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  4. 2020Extracting Training Data from Large Language Modelscited by 2,144
  5. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,615
  6. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  7. 2019TinyBERT: Distilling BERT for Natural Language Understandingcited by 1,153
  8. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 975
  9. 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Headscited by 821
  10. 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 791
  11. 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 633
  12. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 550
38 more
  1. 2022BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Miningcited by 531
  2. 2023DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Modelscited by 505
  3. 2024WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMscited by 460
  4. 2023SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verificationcited by 359
  5. 2024BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domainscited by 299
  6. 2024MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documentscited by 251
  7. 2024ShieldGemma: Generative AI Content Moderation Based on Gemmacited by 224
  8. 2024MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Casescited by 216
  9. 2022Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generationcited by 169
  10. 2023Speculative Decoding with Big Little Decodercited by 163
  11. 2023Accelerating LLM Inference with Staged Speculative Decodingcited by 151
  12. 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptationscited by 144
  13. 2023Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoningcited by 140
  14. 2024A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthinesscited by 125
  15. 2024Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversationscited by 118
  16. 2024Small Language Models: Survey, Measurements, and Insightscited by 109
  17. 2025Small Models Struggle to Learn from Strong Reasonerscited by 109
  18. 2025Accelerating Diffusion LLMs via Adaptive Parallel Decodingcited by 108
  19. 2024On-Device Language Models: A Comprehensive Reviewcited by 101
  20. 2023On Provable Copyright Protection for Generative Modelscited by 97
  21. 2024Decoding-Time Language Model Alignment with Multiple Objectivescited by 90
  22. 2023Cascade Speculative Drafting for Even Faster LLM Inferencecited by 83
  23. 2024Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Modelscited by 83
  24. 2023Learning to Grow Pretrained Models for Efficient Transformer Trainingcited by 81
  25. 2023Offsite-Tuning: Transfer Learning without Full Modelcited by 77
  26. 2024Catastrophic Failure of LLM Unlearning via Quantizationcited by 76
  27. 2025BEST-Route: Adaptive LLM Routing with Test-Time Optimal Computecited by 63
  28. 2024FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuningcited by 57
  29. 2024Understanding the Performance and Estimating the Cost of LLM Fine-Tuningcited by 48
  30. 2024Offline Training of Language Model Agents with Functions as Learnable Weightscited by 47
  31. 2024Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillationcited by 46
  32. 2023LLMCad: Fast and Scalable On-device Large Language Model Inferencecited by 45
  33. 2024SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devicescited by 45
  34. 2024What is the Role of Small Models in the LLM Era: A Surveycited by 41
  35. 2025Distilling LLM Agent into Small Models with Retrieval and Code Toolscited by 41
  36. 2024Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMscited by 34
  37. 2024MobiLlama: Towards Accurate and Lightweight Fully Transparent GPTcited by 34
  38. 2024CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Followingcited by 32

50 most cited of 112 All 112 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2026SpecSteer: Synergizing Local Context and Global Reasoning for Efficient Personalized Generationcited by 6
  2. 2025Generalizing Test-time Compute-optimal Scaling as an Optimizable Graphcited by 4
  3. 2026Do Multimodal RAG Systems Leak Data? A Comprehensive Evaluation of Membership Inference and Image Caption Retrieval Attackscited by 3
  4. 2026Can LoRA Fusion Support Cross-Domain Tasks in Cloud-Edge Collaboration?cited by 0
  5. 2026HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deploymentcited by 0

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