# Efficacy of Various Large Language Models in Generating Smart Contracts

arXiv: https://arxiv.org/abs/2407.11019 · HTML: https://mlsanity.com/paper/2407.11019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 25 of 25)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [Learning to Execute](https://mlsanity.com/paper/1410.4615/) (2014)
- [Learning Performance-Improving Code Edits](https://mlsanity.com/paper/2302.07867/) (2023)
- [Neural Programmer-Interpreters](https://mlsanity.com/paper/1511.06279/) (2015)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [Neuro-Symbolic Program Synthesis](https://mlsanity.com/paper/1611.01855/) (2016)
- [Exploring the Benefits of Training Expert Language Models over Instruction Tuning](https://mlsanity.com/paper/2302.03202/) (2023)
- [GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation](https://mlsanity.com/paper/1906.12192/) (2019)
- [Latent Predictor Networks for Code Generation](https://mlsanity.com/paper/1603.06744/) (2016)
- [On the Robustness of Code Generation Techniques: An Empirical Study on GitHub Copilot](https://mlsanity.com/paper/2302.00438/) (2023)
- [Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives](https://mlsanity.com/paper/2310.01152/) (2023)
- [Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications](https://mlsanity.com/paper/2403.16073/) (2024)
- [Structured Generative Models of Natural Source Code](https://mlsanity.com/paper/1401.0514/) (2014)
- [The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models](https://mlsanity.com/paper/2308.00245/) (2023)
- [NAPS: Natural Program Synthesis Dataset](https://mlsanity.com/paper/1807.03168/) (2018)
- [LLbezpeky: Leveraging Large Language Models for Vulnerability Detection](https://mlsanity.com/paper/2401.01269/) (2024)
- [Benchmarking Language Models for Code Syntax Understanding](https://mlsanity.com/paper/2210.14473/) (2022)
- [Automatic Smart Contract Comment Generation via Large Language Models and In-Context Learning](https://mlsanity.com/paper/2311.10388/) (2023)
- [Recent Developments in Program Synthesis with Evolutionary Algorithms](https://mlsanity.com/paper/2108.12227/) (2021)
- [An Empirical Study of AI-based Smart Contract Creation](https://mlsanity.com/paper/2308.02955/) (2023)

## Built on by (most cited first, 4 of 4)

- [Guiding LLM-based Smart Contract Generation with Finite State Machine](https://mlsanity.com/paper/2505.08542/) (2025)
- [Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm](https://mlsanity.com/paper/2507.08249/) (2025)
- [DMind Benchmark: Toward a Holistic Assessment of LLM Capabilities across the Web3 Domain](https://mlsanity.com/paper/2504.16116/) (2025)
- [On LLM-Assisted Generation of Smart Contracts from Business Processes](https://mlsanity.com/paper/2507.23087/) (2025)

## Abstract (arXiv)

This study analyzes the application of code-generating Large Language Models in the creation of immutable Solidity smart contracts on the Ethereum Blockchain. Other works have previously analyzed Artificial Intelligence code generation abilities. This paper aims to expand this to a larger scope to include programs where security and efficiency are of utmost priority such as smart contracts. The hypothesis leading into the study was that LLMs in general would have difficulty in rigorously implementing security details in the code, which was shown through our results, but surprisingly generally succeeded in many common types of contracts. We also discovered a novel way of generating smart contracts through new prompting strategies.