Constraining Model Output to Defined Formats: A Guide to Structured Generative AI and Tokenization Best Practices

Structured generative AI is a powerful tool that can be used to translate natural language into defined formats such as SQL or JSON. By constraining the generative process to adhere to specific format rules, we can eliminate syntax errors and ensure the accuracy and executability of the output.

To implement structured generative AI, we need to consider the token generation process. By setting the logit values of illegitimate tokens to -inf, we can restrict the model’s choices to only valid tokens. This can be achieved using a logits processor, which modifies the logits before sampling the next token.

In the example provided, we demonstrated how to enforce constraints on a model generating SQL queries. By defining rules for valid tokens to follow each other, we can guide the model to generate executable SQL queries, even without fine-tuning the model specifically for text-to-SQL tasks.

It is important to note that tokenization plays a crucial role in the training and performance of generative AI models. Consistent tokenization of concepts and punctuation is essential to simplify the learning patterns for the model, ultimately improving accuracy and reducing training time.

In summary, structured generative AI offers a valuable approach for translating natural language into defined formats. By enforcing constraints on token generation and ensuring consistent tokenization, we can enhance the accuracy and effectiveness of generative AI models for various applications requiring structured output.