The training-free guidance framework can be extended to other types of discrete data, such as text or tabular data, by adapting the guidance functions to suit the specific characteristics of these data types. For text generation, one could develop guidance functions that assess the semantic or syntactic properties of generated text, such as coherence, sentiment, or specific keyword presence. By leveraging pre-trained language models, one could compute the likelihood of a generated sequence given certain attributes, similar to how molecular properties are evaluated in molecular graph generation.
In the case of tabular data, guidance functions could focus on ensuring that generated rows adhere to specific statistical properties, such as maintaining the distribution of categorical variables or achieving target means and variances for numerical columns. This could involve using statistical tests or machine learning models trained on the original data to evaluate the generated samples against the desired properties. By employing a plug-and-play approach, researchers could easily integrate these guidance functions with existing discrete diffusion models, allowing for flexible and efficient data generation across various domains.