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
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
iPOE generates and optimizes annotation guidelines from explanations to produce interpretable prompts, reporting up to 39% gains over baselines on four datasets with LLM explanations substituting for human ones.
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iPOE: Interpretable Prompt Optimization via Explanations
iPOE generates and optimizes annotation guidelines from explanations to produce interpretable prompts, reporting up to 39% gains over baselines on four datasets with LLM explanations substituting for human ones.