小规模大语言模型在生物污水处理中的科学能力与部署可持续性
Scientific capabilities and deployment sustainability of small-scale LLMs in biological wastewater treatment
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中文总结 AI 辅助
本研究证明领域微调的小规模LLM（BioWater，80亿参数）在生物污水处理中科学能力接近大规模通用模型，且本地部署更具经济与环境可持续性。
中文摘要 AI 辅助
大语言模型（LLMs）正逐渐成为科学助手，然而其计算需求高和领域专业化不足的问题限制了其在环境工程中的可持续部署。在此，我们研究了领域专业化的小规模大语言模型能否在生物污水处理中兼顾科学能力与可持续部署。我们开发了一个基准测试，评估大语言模型的三种科学能力：回顾性认知、理解保真度和前瞻性外推。BioWater（80亿参数，基于专门领域知识进行微调）在理解保真度得分上超过了参与的人类专家，并在回顾性认知和前瞻性外推方面达到了与一个3970亿参数的通用大语言模型相当的性能。人机（Human-BioWater）协作产生了一个科学假设，该假设随后得到了实验室实验的支持，展示了其在推动前瞻性科学研究方面的潜力。我们进一步评估了大语言模型在全球污水处理厂（WWTPs）部署的经济和环境影响。在智能污水处理厂中，随着推理需求的增加，本地部署的小规模大语言模型比基于云的大规模大语言模型更具可持续性。这些发现凸显了领域专业化的小规模大语言模型是通往科学能力强、计算效率高且可持续部署的污水处理人工智能的一条有前景的路径。
英文摘要
Large language models (LLMs) are emerging as scientific assistants, yet their computational demands and limited domain specialization constrain sustainable deployment in environmental engineering. Here, we investigate whether domain-specialized small-scale LLMs can combine scientific capability with sustainable deployment in biological wastewater treatment. We developed a benchmark evaluating three scientific capabilities of LLMs: retrospective cognition, comprehension fidelity, and prospective extrapolation. BioWater (8 billion parameters, fine-tuned on specialized domain knowledge) achieved higher comprehension-fidelity scores than participating human experts and performance comparable to a 397-billion-parameter general-purpose LLM in retrospective cognition and prospective extrapolation. Human-BioWater collaboration generated a scientific hypothesis that was subsequently supported by laboratory experiments, demonstrating its potential to contribute to prospective scientific research. We further evaluated the economic and environmental implications of LLM deployment across global wastewater treatment plants (WWTPs). Locally deployed small-scale LLMs became more sustainable than cloud-based large-scale LLMs as inference demand increased in intelligent WWTPs. These findings highlight domain-specialized small-scale LLMs as a promising pathway towards scientifically capable, computationally efficient, and sustainably deployable artificial intelligence for wastewater treatment.
发表机构
- The Hong Kong University of Science and Technology（香港科技大学）
- Hohai University（河海大学）
机构由 AI 辅助整理，请以论文原文为准。
