Multiple-choice queries synthesized from Hoare triples enable more reliable identification of intended programs than labeled-example supervision in active learning for program disambiguation.
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Schemex is an interactive three-stage AI workflow for schema induction that user studies show produces more actionable schemas than a frontier baseline without loss of generalizability.
Presents GIM model with conductance and influence-capital mechanisms that outperforms baselines, corrects follower-count bias, and finds non-experts exert higher influence than experts on COVID-19 Twitter with higher misinformation spread.
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Choose, Don't Label: Multiple-Choice Query Synthesis for Program Disambiguation
Multiple-choice queries synthesized from Hoare triples enable more reliable identification of intended programs than labeled-example supervision in active learning for program disambiguation.
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Schemex: Discovering Structural Abstractions from Examples
Schemex is an interactive three-stage AI workflow for schema induction that user studies show produces more actionable schemas than a frontier baseline without loss of generalizability.
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Conductance and Influence-Capital: Modeling Online Social Influence
Presents GIM model with conductance and influence-capital mechanisms that outperforms baselines, corrects follower-count bias, and finds non-experts exert higher influence than experts on COVID-19 Twitter with higher misinformation spread.