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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.06203 |
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| _version_ | 1866909531782512640 |
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| author | Sham, Nikumbh Sarthak Chakraborty, Sandip Sural, Shamik |
| author_facet | Sham, Nikumbh Sarthak Chakraborty, Sandip Sural, Shamik |
| contents | While a plethora of machine learning (ML) models are currently available, along with their implementation on disparate platforms, there is hardly any verifiable ML code which can be executed on public blockchains. We propose a novel approach named LMST that enables conversion of the inferencing path of an ML model as well as its weights trained off-chain into Solidity code using Large Language Models (LLMs). Extensive prompt engineering is done to achieve gas cost optimization beyond mere correctness of the produced code, while taking into consideration the capabilities and limitations of the Ethereum Virtual Machine. We have also developed a proof of concept decentralized application using the code so generated for verifying the accuracy claims of the underlying ML model. An extensive set of experiments demonstrate the feasibility of deploying ML models on blockchains through automated code translation using LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generation of Optimized Solidity Code for Machine Learning Models using LLMs Sham, Nikumbh Sarthak Chakraborty, Sandip Sural, Shamik Emerging Technologies Machine Learning While a plethora of machine learning (ML) models are currently available, along with their implementation on disparate platforms, there is hardly any verifiable ML code which can be executed on public blockchains. We propose a novel approach named LMST that enables conversion of the inferencing path of an ML model as well as its weights trained off-chain into Solidity code using Large Language Models (LLMs). Extensive prompt engineering is done to achieve gas cost optimization beyond mere correctness of the produced code, while taking into consideration the capabilities and limitations of the Ethereum Virtual Machine. We have also developed a proof of concept decentralized application using the code so generated for verifying the accuracy claims of the underlying ML model. An extensive set of experiments demonstrate the feasibility of deploying ML models on blockchains through automated code translation using LLMs. |
| title | Generation of Optimized Solidity Code for Machine Learning Models using LLMs |
| topic | Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2503.06203 |