{"type":"ScholarlyArticle","title":"AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts","authors":[{"name":"Maruf Ahmed Mridul","slug":"maruf-ahmed-mridul","given":"Maruf Ahmed","family":"Mridul","affiliation":""},{"name":"Ian Sloyan","slug":"ian-sloyan","given":"Ian","family":"Sloyan","affiliation":""},{"name":"Aparna Gupta","slug":"aparna-gupta","given":"Aparna","family":"Gupta","affiliation":""},{"name":"Oshani Seneviratne","slug":"oshani-seneviratne","given":"Oshani","family":"Seneviratne","affiliation":""}],"abstract":"Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) are reshaping how AI systems extract and organize information from unstructured text. A key challenge is designing AI methods that can incrementally extract, structure, and validate information while preserving hierarchical and contextual relationships. We introduce CDMizer, a template driven, LLM, and RAG-based framework for structured text transformation. By leveraging depth-based retrieval and hierarchical generation, CDMizer ensures a controlled, modular process that aligns generated outputs with predefined schemas. Its template-driven approach guarantees syntactic correctness, schema adherence, and improved scalability, addressing key limitations of direct generation methods. Additionally, we propose an LLM-powered evaluation framework to assess the completeness and accuracy of structured representations. Demonstrated in the transformation of Over-the-Counter (OTC) financial derivative contracts into the Common Domain Model (CDM), CDMizer establishes a scalable foundation for AI-driven document understanding, structured synthesis, and automated validation in broader contexts.","doi":"10.24963/ijcai.2025/1034","url":"https://ijcai.org/proceedings/2025/1034","pdf":"https://ijcai.org/proceedings/2025/1034.pdf","markdown":"https://ijcai.org/proceedings/2025/1034.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1034","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1034,"pages":{"first":9305,"last":9312},"keywords":["Advanced AI4Tech: Generative and LLMs-driven AI4Tech","Domain-specific AI4Tech: AI4Finance"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}