To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending

The article introduces BlendIn, a novel inference-time alignment framework designed to enhance the effectiveness of model guidance during output generation in large language models (LLMs). By transitioning from binary decision-making to hybrid distributions that integrate knowledge from multiple models, BlendIn improves alignment quality by proportionally weighting contributions based on model reliability, resulting in up to a 50% performance improvement on challenging model pairs. This approach is significant for practitioners as it addresses the variability in guidance effectiveness, promoting more efficient and reliable model outputs.