In the realm of technology, large language models(LLMs) have exhibited exceptional capabilities across diverse tasks, including question answering and code generation. At its core, a LLM excels in automatically generating coherent sequences based on given inputs, relying on statistical likelihood. Leveraging this ability, users can prompt these models with language instructions or examples, enabling the execution of various downstream tasks. The advanced techniques of prompting can even facilitate interactions involving the language model, users, and external tools like calculators. However, achieving state-of-the-art performance or tailoring language models to specific tasks often necessitates implementing complex, task-specific programs, which may still rely on ad-hoc interactions.

Language Model Programming (LMP) is an emerging discipline that is gaining traction to tackle those challenges. LMP represents a significant advancement in language model prompting, transitioning from pure text prompts to an intuitive combination of text prompting and scripting. Furthermore, LMP empowers users to specify constraints on the language model’s output, facilitating effortless adaptation to a multitude of tasks while abstracting the inner workings of the language model and providing high-level semantics. Recently, researchers from the prestigious ETH Zurich released LMQL, a query language for LLMs that builds on the principles of LMP...