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Found 7 papers, 3 papers with code
Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
New capabilities in foundation models are owed in large part to massive, widely-sourced, and under-documented training data collections.
Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling
Finally, we find that learning with stories shows a higher retention rate for non-native speakers in the follow-up assessment.
Verifiable evaluations of machine learning models using zkSNARKs
In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value.
LePaRD: A Large-Scale Dataset of Judges Citing Precedents
We present the Legal Passage Retrieval Dataset LePaRD.
The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI
The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners.
The Law and NLP: Bridging Disciplinary Disconnects
Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP).
Art and the science of generative AI: A deeper dive
A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation.
