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Efficient Large Language Models: A Survey
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Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding and language generation, and thus have the potential to make a substantial impact on our society. Such capabilities, however, come with the considerable resources they demand, highlighting the strong need to develop effective techniques for addressing their efficiency challenges. In this survey, we provide a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from model-centric, data-centric, and framework-centric perspective, respectively. We have also created a GitHub repository where we organize the papers featured in this survey at https://github.com/AIoT-MLSys-Lab/Efficient-LLMs-Survey. We will actively maintain the repository and incorporate new research as it emerges. We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of efficient LLMs research and inspire them to contribute to this important and exciting field.
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Cited by 15 Pith papers
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OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents
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Token Reduction Is Not Cost Reduction
Token reduction of tool output does not reliably lower billed coding-agent cost; prompt-cache traffic dominates, and aggressive compression can also destroy task success.
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What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents
KV-cache eviction, prompt compression, recurrent state bounding, and agent memory consolidation are unified as one rate-distortion problem with a shared lower bound, shared failure mode, and transferable mechanisms.
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Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection
A decoding pipeline reuses cached tokens and anneals sampling temperature to accelerate self-consistency hallucination detection by up to 3x without meaningful AUROC loss.
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EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning
EAT, an attention-guided diffusion reinforcement learning scheduler for collaborative edge AIGC, reduces Stable Diffusion inference latency by 56-74% versus baselines while keeping CLIP quality nearly unchanged.
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Direct Behavior Optimization: Unlocking the Potential of Lightweight LLMs
A gradient-free Monte Carlo tree search over JSON key-step plans produces few-shot demonstrations that let LLaMA3-8B and LLaMA3.2-3B outperform GPT-3.5 on most of seven BIG-Bench Hard tasks.
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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
DaMoC combines data filtering, token compression, and layer pruning to select the best LLM for domain fine-tuning, claiming ~20x faster training while preserving model rankings.
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TASE: Token Awareness and Structured Evaluation for Multilingual Language Models
TASE benchmark shows LLMs lag humans on token-level and structural language tasks across Chinese, English, and Korean despite strong high-level performance.
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Semantic Scheduling for LLM Inference
A semantic scheduler for LLM inference uses urgency labels and estimated remaining compute to cut waiting times for urgent requests, tested on emergency medical data.
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Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search
Small language models achieve higher accuracy on math, coding, and logic benchmarks when their prompts contain LLM-generated reasoning blueprints and a per-model, per-task searched template.
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The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy
Medical agents should be scaled mainly by richer clinical environments and self-evolution loops, not parameter growth alone, under a three-level autonomy taxonomy.
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CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking process
CHS-SQL combines beam search with model-internal-confidence filtering in schema linking to improve small-model text-to-SQL accuracy on Spider and BIRD.
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NeurIPS 2025 E2LM Competition : Early Training Evaluation of Language Models
The authors propose a competition with new scoring metrics to find benchmarks that give clean early-training signals for small language models, and show MMLU-var outperforms MMLU as a baseline.
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SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling
SkipGPT learns per-token, per-module routers that dynamically skip attention and MLP blocks, then uses LoRA to recover most of the original model's quality.
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Toward Efficient Agents: Memory, Tool learning, and Planning
A survey that organizes efficiency techniques for LLM agents into memory, tool learning, and planning, and consolidates benchmarks and metrics for measuring cost-performance trade-offs.
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