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This paper presents COBART, a method that fine-tunes BART with prefix control tokens to generate ad headlines with controllable length and optimized click-through rate (CTR), achieving a 25.82% improvement in ROUGE-L and 5.82% in estimated CTR over previous baselines.
This paper systematically studies the limitations of steering vectors for controlled text generation, finding that their effectiveness varies across traits, degrades on task transfer, and suffers from composition tradeoffs.
This paper investigates how fine-tuning vision-language models to produce dense coordinate lists creates a controllable interference surface, finding that duplicate pressure can be removed without sacrificing localization accuracy.
This paper proposes a neuron-level intervention method to identify gender-specific neurons in language models (feminine, masculine, gender-neutral) and steer sentence generation toward a target gender form while preserving meaning, with experiments showing precise control and bias mitigation.
The paper identifies off-manifold drift in guided flow models under compositional rewards and proposes Conflict-Aware Additive Guidance (CAR), a lightweight method that dynamically resolves gradient conflicts to improve generation fidelity without retraining.
This paper introduces a novel adaptive scheduler for steering discrete diffusion language models using sparse autoencoders, demonstrating that targeting interventions based on when specific attributes commit improves control quality and strength over uniform methods.