Introducing LMQL: A New Programming Language that Interacts with Large Language Models

Saturday - April 29 2023, 06:06 UTC - 1 year ago

Researchers from ETH Zurich have developed a new programming language called LMQL that increases user control and interaction with large language models like ChatGPT. This programming language combines natural and programming language to guide the language models in a more precise way. LMQL enables a novel way of programming and provides safety constraints to prevent unexpected or controversial outputs.

Researchers from ETH Zurich created a new open-source platform and programming language called LMQL. This programming language makes it easier, cheaper, and safer to interact with large language models like ChatGPT.

The language model ChatGPT is widely known by people in the tech community and the general public. Through a chatbot, one can interact with these language models directly without needing any programming skills. Instead of using code, the user can input commands and questions with natural language.

Sometimes this interaction works well, and the desired result appears on-screen. However, at other times the language model does not understand the command, and the generated output can turn out unexpectedly or even unsatisfactory. What people usually do as a response is that they follow up with another query. ChatGPT will then try to correct its mistakes and adapt its answer. This way of using a language model is messy and random, and it can take quite some time to get the preferred outcome.

To solve this problem, ETH researchers now developed a new programming language and open-source platform called LMQL (Language Model Query Language). Utilizing LMQL allows a user to interact with large language models like ChatGPT in a more elevated and controlled way. This programming language enables a novel way of programming and is a new form of computer-human interaction because the user can directly talk to and instruct the computer.

LMQL is the first language that combines natural and programming languages' power to interact with these large language models. For simple queries, it is sufficient to guide ChatGPT using natural language. However, for more complex and specific tasks, such as creating a database or analyzing data, it is essential to instruct the language model precisely. Therefore, the formalism of programming languages is needed to guide the language model with formal constructs to ensure the user gets the desired output.

Martin Vechev, Professor of Computer Science and one of the creators, clarifies: "Essentially, it is a much more concise way to get what you want. Decreasing the necessary exchanges with the language model also reduces the costs of interacting with the model, which can be quite expensive. Using LMQL increases the chances of getting the desired output. It sometimes even makes it possible to get a result you would have never gotten otherwise because you can formulate your query more accurately." .

--- A key advantage --- .

The datasets that large language models are trained with and based on are so massive that the user cannot control and comprehend what happens internally within the model. Because of that, these models can sometimes produce unexpected or controversial outputs. According to the researchers, one of the substantial problems for people using these large language models is that they cannot understand why a certain result was produced and how to prevent it.

LMQL allows its user to express safety constraints that can help guide the model in the right direction and try to steer it away from unwanted or unexpected outputs. "Using LMQL, you can restrict your language model to strictly follow a specific framework you designed. This allows you to better control how the language model behaves. Of course, full guaranteed prevent unexpected results is still impossible with large language models, but LMQL can provide more structure and guidance", says Martin Vechev.