Uploaded April 2024 | Updated September 2026, 1 day ago
Speakers:
Marc Fisher: https://www.sri.inf.ethz.ch/people/marc
Luca Beurer-Kellner (ETH Zürich): A Ph.D. student in the Department of Computer Science at ETH Zurich, under the supervision of Professor Martin Vechev. He holds a Bachelor's degree in Computer Science from Humboldt University of Berlin and a Master's degree from ETH Zurich. Luca's research focuses on the intersection of programming languages and machine learning. Most recently, he worked on controlled and constrained text generation with Large Language Models. He is the lead author of the LMQL (Language Model Query Language) paper in 2023, presenting an innovative query language for LLMs, enabling precise control over generated text.
Abstract:
Large language models have recently demonstrated outstanding performance on a wide range of tasks. However, to obtain state-of-the-art performance or adapt language models for specific tasks, complex task- and model-specific programs have to be implemented, which may still require ad-hoc interaction or laborious result parsing.
To address this we implement LMQL (short for Language Model Query Language). LMQL is a programming language for language model interaction and generalizes natural language prompting, making it more expressive while remaining accessible. For this, LMQL builds on top of Python, allowing users to express natural language prompts as well as control flow, allowing the user to steer the LLM's reasoning process. In LMQL, users can specify high-level, logical constraints over the language model output. These constraints are then automatically converted into token-level prediction masks, which can be enforced eagerly during text generation. This allows to enforce constraints strictly, making it impossible for the model to generate content that does not satisfy the requirements. Try it now on lmql.ai
Speakers:
Marc Fisher: https://www.sri.inf.ethz.ch/people/marc
Luca Beurer-Kellner (ETH Zürich): A Ph.D. student in the Department of Computer Science at ETH Zurich, under the supervision of Professor Martin Vechev. He holds a Bachelor's degree in Computer Science from Humboldt University of Berlin and a Master's degree from ETH Zurich. Luca's research focuses on the intersection of programming languages and machine learning. Most recently, he worked on controlled and constrained text generation with Large Language Models. He is the lead author of the LMQL (Language Model Query Language) paper in 2023, presenting an innovative query language for LLMs, enabling precise control over generated text.
Abstract:
Large language models have recently demonstrated outstanding performance on a wide range of tasks. However, to obtain state-of-the-art performance or adapt language models for specific tasks, complex task- and model-specific programs have to be implemented, which may still require ad-hoc interaction or laborious result parsing.
To address this we implement LMQL (short for Language Model Query Language). LMQL is a programming language for language model interaction and generalizes natural language prompting, making it more expressive while remaining accessible. For this, LMQL builds on top of Python, allowing users to express natural language prompts as well as control flow, allowing the user to steer the LLM's reasoning process. In LMQL, users can specify high-level, logical constraints over the language model output. These constraints are then automatically converted into token-level prediction masks, which can be enforced eagerly during text generation. This allows to enforce constraints strictly, making it impossible for the model to generate content that does not satisfy the requirements. Try it now on lmql.ai
