Transparent Screening for LLM Inference and Training Impacts
This addresses the need for better environmental impact assessment in AI for researchers and practitioners, though it is incremental as it provides a proxy method rather than direct measurement.
The paper tackles the problem of estimating environmental impacts of large language models under limited observability by introducing a transparent screening framework that converts application descriptions into bounded environmental estimates and supports a comparative online observatory, resulting in improved comparability, transparency, and reproducibility without claiming direct measurement.
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.