TL;DR
Various deep learning architectures including the domain-specific pre-trained transformers for the task of legal sentence classification are explored, finding that domain-specific transformer models lead the row.
Legal AI systems require accuracy and verifiable reasoning, yet under-resourced languages lack the specialized models needed to meet these standards. This challenge is particularly acute for statute-based civil law systems like Vietnam’s, where the core task is interpreting and applying codified statutes rather than ma...
In this paper, we evaluate open-source generative LLMs on legal Natural Language Inference (NLI). Legal inspectorial processes take place in specific domains and often deal with confidential data. This creates a need for working with local models that do not require labeled training data. We evaluate our models on the...
Text content is the dominant factor in annotation decisions, far outweighing annotator demographics, and that content-focused SHAP explanations are more effective than demographic persona prompting for guiding LLM annotations, showing that explainability methods can improve both the reliability and the transparency of...
A sentence-level benchmark for evaluating the ability of LLMs to classify interpretive canons as articulated by Larenz in the tradition of Savigny is contributed, operationalizing this conception of interpretation as classification criteria.
New research can help AI models understand crystalline materials in 3D and reason about their properties.
General Science
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
A Blog post by Swiss AI Initiative on Hugging Face
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