揭示大模型资源消耗威胁，助力提升效率与可持续性
Resource Consumption Threats in Large Language Models
- 系统梳理大模型资源消耗威胁的全链条机制
- 指出过度生成导致效率下降与服务不可持续
- 适合关注LLM优化与安全的开发者与研究者
受限于有限且昂贵的计算基础设施，资源效率是大型语言模型（LLMs）的关键需求。高效的LLMs能提升服务商的服务容量，降低用户延迟和API成本。近期的资源消耗威胁引发过度生成，恶化模型效率，损害服务可用性和经济可持续性。本文对大模型资源消耗威胁进行系统综述，通过明确该新兴领域的范围，从威胁诱导到机制理解与缓解，建立统一视角。目标是厘清这一新兴领域的问题格局，为特征刻画与缓解措施提供更清晰的基础。
原文摘要 · Abstract (English)
Given limited and costly computational infrastructure, resource efficiency is a key requirement for large language models (LLMs). Efficient LLMs increase service capacity for providers and reduce latency and API costs for users. Recent resource consumption threats induce excessive generation, degrading model efficiency and harming both service availability and economic sustainability. This survey presents a systematic review of threats to resource consumption in LLMs. We further establish a unified view of this emerging area by clarifying its scope and examining the problem along the full pipeline from threat induction to mechanism understanding and mitigation. Our goal is to clarify the problem landscape for this emerging area, thereby providing a clearer foundation for characterization and mitigation.
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