Transparent Screening for LLM Inference and Training Impacts
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866917427088982016 |
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| author | Pachot, Arnault Petit, Thierry |
| author_facet | Pachot, Arnault Petit, Thierry |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_19757 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Transparent Screening for LLM Inference and Training Impacts Pachot, Arnault Petit, Thierry Machine Learning Artificial Intelligence Computation and Language 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. |
| title | Transparent Screening for LLM Inference and Training Impacts |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2604.19757 |