PRAXA is a compositional grammar for what-if analysis with data, model, and interaction primitives, encoded in PSL, shown to reconstruct existing workflows and enable new multi-step compositions.
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CAPE produces spatially grounded natural-language explanations for document layouts using pattern detection and multi-level context, rated more helpful than content-only baselines in a user study.
LLM-augmented semantic steering lets analysts reshape text embedding projections by providing semantic groupings that an LLM externalizes and extends to improve alignment with intended structures using minimal interaction.
citing papers explorer
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PRAXA: A Grammar for What-If Analysis
PRAXA is a compositional grammar for what-if analysis with data, model, and interaction primitives, encoded in PSL, shown to reconstruct existing workflows and enable new multi-step compositions.
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Context-Aware Explanations for Spatialized Document Layouts
CAPE produces spatially grounded natural-language explanations for document layouts using pattern detection and multi-level context, rated more helpful than content-only baselines in a user study.
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LLM-Augmented Semantic Steering of Text Embedding Projection Spaces
LLM-augmented semantic steering lets analysts reshape text embedding projections by providing semantic groupings that an LLM externalizes and extends to improve alignment with intended structures using minimal interaction.