为大模型推理与训练影响提供可审计的透明评估框架
Transparent Screening for LLM Inference and Training Impacts
- 将应用描述转为可量化的环境影响估算
- 支持对主流模型进行在线对比观测
- 适用于关注模型碳足迹的开发者与研究者
本文提出一种透明筛选框架，用于在可观测性受限的情况下估算当前大语言模型的推理与训练影响。该框架将自然语言的应用描述转化为有界环境估算，并支持对当前市场模型的对比在线观测。不同于对封闭专有服务的直接测量，该方法提供可审计、源链接的代理评估路径，旨在提升可比性、透明度与可复现性。
原文摘要 · Abstract (English)
This paper presents a transparent screening framework for estimating inference and training impacts of current large language models under limited observability. The framework converts natural-language application descriptions into bounded environmental estimates and supports a comparative online observatory of current market models. Rather than claiming direct measurement for opaque proprietary services, it provides an auditable, source-linked proxy methodology designed to improve comparability, transparency, and reproducibility.
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