{"type":"ScholarlyArticle","title":"Using Large Language Models to Improve Query-based Constraint Acquisition","authors":[{"name":"Younes Mechqrane","slug":"younes-mechqrane","given":"Younes","family":"Mechqrane","affiliation":"Ai movement -International Artificial Intelligence Center of Morocco - University Mohammed VI Polytechnic , Rabat , Morocco"},{"name":"Christian Bessiere","slug":"christian-bessiere","given":"Christian","family":"Bessiere","affiliation":"CNRS , University of Montpellier , France"},{"name":"Ismail Elabbassi","slug":"ismail-elabbassi","given":"Ismail","family":"Elabbassi","affiliation":"Ai movement -International Artificial Intelligence Center of Morocco - University Mohammed VI Polytechnic , Rabat , Morocco"}],"abstract":"Most active constraint acquisition systems suffer from two weaknesses. They require the explicit generation of the set of potential constraints (the  bias), whose size can be prohibitive for practical use of these systems, and the answers to queries contain little information. In this paper, we introduce ACQNOGOODS, an active learning schema  that does not require the construction of a bias. We then propose LLMACQ, an active learning system that incorporates a Large Language Model component in the ACQNOGOODS schema. LLMACQ interprets the user’s answers given in natural language, leading to more informative communication. As our experiments show, the non requirement of a bias in extension combined to the higher level communication with the user allow LLMACQ to learn constraints of any arity and to dramatically decrease the number of queries.","doi":"10.24963/ijcai.2024/212","url":"https://ijcai.org/proceedings/2024/212","pdf":"https://ijcai.org/proceedings/2024/0212.pdf","markdown":"https://ijcai.org/proceedings/2024/212.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/212","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":212,"pages":{"first":1916,"last":1925},"keywords":["Constraint Satisfaction and Optimization: CSO: Constraint learning and acquisition"],"videos":[],"references":[{"position":1,"raw":"[Addi et al., 2018] Hajar Ait Addi, Christian Bessiere, Re- douane Ezzahir, and Nadjib Lazaar. 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