mlsanityData to 14 Oct 2026

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

arXiv 2506.01063 ↗First posted 1 Jun 2025cs.IRcited by 2

On the map: Retrieval-augmented generation → Legal and financial RAG

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.

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.

Builds on

6 of 24 references.

References · 6

  1. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  2. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  3. 2021Retrieval Augmented Code Generation and Summarizationcited by 149
  4. 2024Unsupervised Evaluation of Code LLMs with Round-Trip Correctnesscited by 23
  5. 2024Using Large Language Models for Generating Smart Contracts for Health Insurance from Textual Policiescited by 5
  6. 2018Temporal Aspects of Smart Contracts for Financial Derivativescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAGcited by 1
  2. 2025Curriculum-Guided Reinforcement Learning for Synthesizing Gas-Efficient Financial Derivatives Contractscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Benchmarking Multi-Agent LLM Architectures for Financial Document Processing: A Comparative Study of Orchestration Patterns, Cost-Accuracy Tradeoffs and Production Scaling Strategies
  2. 2024OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain
  3. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
  4. 2024T-RAG: Lessons from the LLM Trenches
  5. 2024RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis -- A Case Study for the Semiconductor Sector
  6. 2025Adaptation of Embedding Models to Financial Filings via LLM Distillation
  7. 2026MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
  8. 2025Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models