The Min-K%++ method can be extended to other types of language models beyond autoregressive LLMs by adapting the token-wise scoring mechanism to suit the specific characteristics of the target model. For instance, for non-autoregressive models or models with different architectures, the normalization factors used in Min-K%++ may need to be adjusted to capture the relevant information for those models. Additionally, the concept of comparing the token probability with the distribution over the vocabulary can be generalized to different types of language models by considering the unique properties of each model.