Transparent Screening for LLM Inference and Training Impacts

arXiv cs.LG / 4/23/2026

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Key Points

The paper introduces a transparent framework that estimates how LLMs may affect inference and training outcomes even when direct observability is limited.
It translates natural-language descriptions of an application into bounded “environmental” estimates to support comparison across different model deployments.
The authors propose a comparative online observatory approach to evaluate current market models using auditable, source-linked proxy methods.
Instead of claiming direct measurement of opaque proprietary services, the framework focuses on improving comparability, transparency, and reproducibility through proxy-based estimates.

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.