Contrastive Decoding

Improve LLM Generation with Contrastive Decoding

Improve LLM Generation with Contrastive Decoding A language model can assign high probability to text that is fluent but bland, repetitive, or overly driven by common patterns. Sampling adds variety, but increasing randomness can also admit weak continuations. Contrastive decoding takes a different route: compare a stronger model with a weaker reference model at each generation step, then favor tokens that the stronger model supports more distinctly. The method changes decoding rather than model parameters. It can therefore be useful when you control inference for compatible models and want to experiment with generation quality without another training run. The extra model pass is not free, and the method needs a plausibility guard to avoid promoting strange tokens.