CRGC models instructions as constraint graphs, identifies bridge constraints, and cuts violations by 39% on three datasets while preserving reasoning performance.
and Kawaguchi, Kenji and Shieh, Michael and He, Junxian
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KG-FairDiff is an inference-time framework that uses a knowledge graph to guide prompt refinement and reduce gender, race, age, and intersectional biases in text-to-image generation while preserving semantics.
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Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
CRGC models instructions as constraint graphs, identifies bridge constraints, and cuts violations by 39% on three datasets while preserving reasoning performance.
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KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation
KG-FairDiff is an inference-time framework that uses a knowledge graph to guide prompt refinement and reduce gender, race, age, and intersectional biases in text-to-image generation while preserving semantics.
- iPOE: Interpretable Prompt Optimization via Explanations