# Using Large Language Models for Generating Smart Contracts for Health Insurance from Textual Policies

arXiv: https://arxiv.org/abs/2407.07019 · HTML: https://mlsanity.com/paper/2407.07019/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 9 Jul 2024.

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

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [LEVER: Learning to Verify Language-to-Code Generation with Execution](https://mlsanity.com/paper/2302.08468/) (2023)
- [ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports](https://mlsanity.com/paper/2212.14882/) (2022)

## Built on by (most cited first, 5 of 5)

- [Semantic Interoperability on Blockchain by Generating Smart Contracts Based on Knowledge Graphs](https://mlsanity.com/paper/2409.12171/) (2024)
- [AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts](https://mlsanity.com/paper/2506.01063/) (2025)
- [MSA at SemEval-2025 Task 3: High Quality Weak Labeling and LLM Ensemble Verification for Multilingual Hallucination Detection](https://mlsanity.com/paper/2505.20880/) (2025)
- [Legal Compliance Evaluation of Smart Contracts Generated By Large Language Models](https://mlsanity.com/paper/2506.00943/) (2025)
- [A Benchmark for Gap and Overlap Analysis as a Test of KG Task Readiness](https://mlsanity.com/paper/2604.10853/) (2026)

## Abstract (arXiv)

We explore using Large Language Models (LLMs) to generate application code that automates health insurance processes from text-based policies. We target blockchain-based smart contracts as they offer immutability, verifiability, scalability, and a trustless setting: any number of parties can use the smart contracts, and they need not have previously established trust relationships with each other. Our methodology generates outputs at increasing levels of technical detail: (1) textual summaries, (2) declarative decision logic, and (3) smart contract code with unit tests. We ascertain LLMs are good at the task (1), and the structured output is useful to validate tasks (2) and (3). Declarative languages (task 2) are often used to formalize healthcare policies, but their execution on blockchain is non-trivial. Hence, task (3) attempts to directly automate the process using smart contracts. To assess the LLM output, we propose completeness, soundness, clarity, syntax, and functioning code as metrics. Our evaluation employs three health insurance policies (scenarios) with increasing difficulty from Medicare's official booklet. Our evaluation uses GPT-3.5 Turbo, GPT-3.5 Turbo 16K, GPT-4, GPT-4 Turbo and CodeLLaMA. Our findings confirm that LLMs perform quite well in generating textual summaries. Although outputs from tasks (2)-(3) are useful starting points, they require human oversight: in multiple cases, even "runnable" code will not yield sound results; the popularity of the target language affects the output quality; and more complex scenarios still seem a bridge too far. Nevertheless, our experiments demonstrate the promise of LLMs for translating textual process descriptions into smart contracts.