Prompts for public-sector LLMs encode value-laden decisions and should be governed through community-maintained Prompt Commons repositories with provenance, licensing, and moderation.
On the standardization of behavioral use clauses and their adoption for responsible licensing of ai.arXiv preprint arXiv:2402.05979,
4 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
SemFin combines model configuration files with repository tags to impute missing metadata across 317k PTLMs, outperforming propagation baselines by up to 31.4% and expanding reuse and license lineage chains on 167k models.
Generative AI systems arise from statistical data processing that produces human-like outputs, creating a mismatch with traditional computer expectations and positioning educational researchers to lead in studying and applying them.
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Prompts for Public-Sector LLMs Should Be Governed as Commons
Prompts for public-sector LLMs encode value-laden decisions and should be governed through community-maintained Prompt Commons repositories with provenance, licensing, and moderation.
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A governance horizon for ethical-use constraints in open-weight AI models
Ethical constraint evidence on open-weight AI models decays with a half-life of 1.31 derivation steps on Hugging Face, creating a governance horizon at seven generations where 80% of models lack traceable information.
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Towards Imputation of Pre-Trained Language Model Metadata using Semantic Fingerprinting
SemFin combines model configuration files with repository tags to impute missing metadata across 317k PTLMs, outperforming propagation baselines by up to 31.4% and expanding reuse and license lineage chains on 167k models.
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Generative AI Technologies, Techniques & Tensions: A Primer
Generative AI systems arise from statistical data processing that produces human-like outputs, creating a mismatch with traditional computer expectations and positioning educational researchers to lead in studying and applying them.