基于大语言模型的环境分析能耗：LLM与领域产品
The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Products
AI总结：
本文研究了领域特定RAG系统与通用LLM在能耗上的差异，通过分解工作流程评估了ChatNetZero和ChatNDC的能耗，发现领域设计显著影响能耗与输出质量。
摘要：
随着大语言模型（LLM）在特定领域应用（如气候变化和环境研究）中的普及，理解其能耗变得至关重要。本文评估了两个基于LLM的气候分析聊天机器人（ChatNetZero和ChatNDC）与通用GPT-4o-mini模型在推理时间能耗的差异。通过分解每个工作流程为检索、生成和幻觉检查组件，我们估计了实际用户查询下的能耗，并测试了不同时间和地理接入位置的影响。研究结果表明，领域特定RAG系统的能耗强烈依赖于其设计。更具代理性的流程显著增加推理时间能耗，特别是在用于额外准确性或验证检查时，尽管可能不会带来成比例的响应质量提升。虽然需要进一步研究以更稳健地测试这些初始发现，但本研究提供了新的理解，说明领域特定LLM产品设计如何影响能耗和输出质量。
英文摘要：
As large language models (LLMs) are increasingly used in domain-specific applications, including climate change and environmental research, understanding their energy footprint has become an important concern. The growing adoption of retrieval-augmented (RAG) systems for climate-domain specific analysis raises a key question: how does the energy consumption of domain-specific RAG workflows compare with that of direct generic LLM usage? Prior research has focused on standalone model calls or coarse token-based estimates, while leaving the energy implications of deployed application workflows insufficiently understood. In this paper, we assess the inference-time energy consumption of two LLM-based climate analysis chatbots (ChatNetZero and ChatNDC) compared to the generic GPT-4o-mini model. We estimate energy use under actual user queries by decomposing each workflow into retrieval, generation, and hallucination-checking components. We also test across different times of day and geographic access locations. Our results show that the energy consumption of domain-specific RAG systems depends strongly on their design. More agentic pipelines substantially increase inference-time energy use, particularly when used for additional accuracy or verification checks, although they may not yield proportional gains in response quality. While more research is needed to further test these initial findings more robustly across models, environments and prompting structures, this study provides a new understanding on how the design of domain-specific LLM products affects both the energy footprint and quality of output.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
