# AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts

arXiv: https://arxiv.org/abs/2506.01063 · HTML: https://mlsanity.com/paper/2506.01063/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Placed in

- Retrieval-augmented generation / [Legal and financial RAG](https://mlsanity.com/rag/legal-and-financial-rag/)

## Builds on (most cited first, 6 of 6)

- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Retrieval Augmented Code Generation and Summarization](https://mlsanity.com/paper/2108.11601/) (2021)
- [Unsupervised Evaluation of Code LLMs with Round-Trip Correctness](https://mlsanity.com/paper/2402.08699/) (2024)
- [Using Large Language Models for Generating Smart Contracts for Health Insurance from Textual Policies](https://mlsanity.com/paper/2407.07019/) (2024)
- [Temporal Aspects of Smart Contracts for Financial Derivatives](https://mlsanity.com/paper/1805.11677/) (2018)

## Built on by (most cited first, 2 of 2)

- [CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG](https://mlsanity.com/paper/2605.08333/) (2026)
- [Curriculum-Guided Reinforcement Learning for Synthesizing Gas-Efficient Financial Derivatives Contracts](https://mlsanity.com/paper/2509.23976/) (2025)

## Abstract (arXiv)

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 schema. 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.