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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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.
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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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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.