Researchers developed a more efficient way to control the outputs of a large language model, guiding it to generate text that adheres to a certain structure, like a programming language, and remains error free.
Programmers can now use large language models to generate computer code more quickly. However, this only makes programmers' lives easier if that code follows the rules of the programming language and doesn't cause a computer to crash.
In the long run, this new architecture could help nonexperts control AI-generated content. For instance, it could allow businesspeople to write complex queries in SQL, a language for database manipulation, using only natural language prompts. "It is much easier to enforce structure than meaning. We can quickly check whether something is in the right programming language, but to check its meaning you have to execute the code. Our work is also about dealing with these different types of information," Loula says.
Each output is given a weight that represents how likely it is to be structurally valid and semantically accurate. At each step in the computation, the model focuses on those with higher weights and throws out the rest. In Python code generation, for instance, the researchers' architecture enabled a small, open-source model to outperform a specialized, commercial closed-source model that is more than double its size.
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