LLM News

Researchers have developed a parameter-efficient method to control LLM personalities at inference time using Sequential Adaptive Steering (SAS), which orthogonalizes steering vectors to avoid interference when adjusting multiple traits simultaneously. The approach allows users to modulate the Big Five personality dimensions by adjusting numerical coefficients without retraining models.

Researchers introduce Conditioned Activation Transport (CAT), a technique that reduces unsafe content generation in text-to-image models during inference without the quality degradation seen in previous linear steering approaches. The method uses a contrastive dataset of 2,300 safe/unsafe prompt pairs and geometry-based conditioning to target only unsafe activation regions.