Transparent Screening for LLM Inference and Training Impacts
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Knowledge Transfer for Melanoma Screening with Deep Learning
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a…
Deep Learningimage-classificationImage ClassificationMedical Image Analysis+2AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs
Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen, or assess, many studies for inclusion in the review. Several tools hav…
Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks
Tuberculosis persists as a global health crisis, especially in resource-limited populations and remote regions, with more than 10 million individuals newly infected annually. It stands as a stark symbol of inequity in pu…
Towards Example-Based NMT with Multi-Levenshtein Transformers
Retrieval-Augmented Machine Translation (RAMT) is attracting growing attention. This is because RAMT not only improves translation metrics, but is also assumed to implement some form of domain adaptation. In this contrib…
Domain AdaptationImitation LearningMachine TranslationNMT+2Evidential Reasoning Advances Interpretable Real-World Disease Screening
Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical images suffer from limited interpretability and suboptimal performance. T…