Transparent Screening for LLM Inference and Training Impacts

TL;DR

This paper introduces a transparent screening framework that estimates the impacts of large language models on inference and training, enhancing transparency and comparability without needing direct access to proprietary models.

Contribution

It develops a proxy methodology converting natural-language descriptions into environmental estimates, supporting an online observatory for market models.

Findings

Provides a source-linked, auditable proxy for LLM impacts.

Enables comparison of different models without direct measurement.

Supports transparency and reproducibility in LLM impact assessment.

Abstract

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.