Teaching Machines to Code: Smart Contract Translation with LLMs
作者: Rabimba Karanjai, Lei Xu, Weidong Shi
分类: cs.SE, cs.AI
发布日期: 2024-03-13
💡 一句话要点
提出SolMover以解决智能合约翻译问题
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 智能合约 语言模型 代码翻译 区块链 机器学习 自然语言处理 编程语言
📋 核心要点
- 现有方法在智能合约翻译中面临挑战，尤其是翻译为LLM未接触过的语言。
- SolMover通过两个LLM的协同工作，理解编码原则并进行智能合约翻译。
- 实验结果表明，SolMover在性能上显著优于gpt-3.5-turbo-1106，并超越了其他竞争模型。
📝 摘要（中文）
大型语言模型（LLMs）的出现标志着人工智能领域的重要里程碑，其能力在多个领域往往与人类专家相匹敌。尽管在编程代码翻译方面已有进展，但智能合约翻译，尤其是翻译为LLM未曾接触过的语言，仍然较少探索。本文提出了一种开创性的方法SolMover，利用两个不同的LLM在统一框架下协同工作，旨在理解编码原则并将其应用于将Solidity编写的智能合约翻译为资源有限的Move语言。我们的研究深入评估了LLMs模仿人类学习过程的能力，实验证明SolMover在性能上显著优于gpt-3.5-turbo-1106，并在与Palm2和Mixtral-8x7B-Instruct等竞争者的比较中取得了更好的结果。
🔬 方法详解
问题定义：本研究旨在解决智能合约翻译中存在的困难，尤其是将Solidity代码翻译为Move语言时，现有方法在处理未接触语言时的局限性。
核心思路：SolMover的核心思路是利用两个不同的LLM，一个负责理解新语言的编码规范，另一个则专注于代码翻译，从而实现高效的智能合约翻译。
技术框架：整体架构包括两个主要模块：第一个LLM解析新语言的编码约定，生成翻译蓝图；第二个LLM根据蓝图进行代码翻译。
关键创新：SolMover的创新在于其双LLM协同工作机制，能够有效模仿人类学习过程，提升翻译质量，与传统单一模型方法相比具有本质区别。
关键设计：在模型设计中，采用了特定的参数设置和损失函数，以优化翻译效果，并确保两个LLM之间的有效协作。具体细节包括对新语言的编码规范的深入解析和对翻译结果的质量评估。
🖼️ 关键图片
📊 实验亮点
实验结果显示，SolMover在智能合约翻译任务中显著提升了性能，超越了gpt-3.5-turbo-1106，并在与Palm2和Mixtral-8x7B-Instruct的比较中表现出更优的翻译质量，验证了其有效性和创新性。
🎯 应用场景
该研究的潜在应用领域包括区块链智能合约开发、跨链技术以及编程语言翻译工具。通过提高智能合约的翻译质量，SolMover能够帮助开发者更高效地进行跨语言开发，降低开发成本，提升代码的可维护性和安全性，未来可能对智能合约生态系统产生深远影响。
📄 摘要（原文）
The advent of large language models (LLMs) has marked a significant milestone in the realm of artificial intelligence, with their capabilities often matching or surpassing human expertise in various domains. Among these achievements, their adeptness in translation tasks stands out, closely mimicking the intricate and preliminary processes undertaken by human translators to ensure the fidelity and quality of the translated content. Despite the advancements in utilizing LLMs for translating programming code across different languages, the domain of smart contract translation, particularly into languages not previously encountered by the LLM, remains largely unexplored. In our research, we present a pioneering approach, SolMover, which harnesses the synergy of two distinct LLMs within a unified framework. This framework is designed to grasp coding principles and apply this understanding to the translation of code into an unfamiliar language. Our study delves into the capacity of LLMs to mimic human learning processes, offering an in-depth evaluation of our methodology for converting smart contracts written in Solidity to Move, a language with limited resources. The framework employs one LLM to decipher coding conventions for the new language, creating a blueprint for the second LLM, which, lacking planning abilities, possesses coding expertise. The empirical evidence from our experiments suggests that SolMover substantially enhances performance compared to gpt-3.5-turbo-1106, and achieves superior results over competitors such as Palm2 and Mixtral-8x7B-Instruct. Additionally, our analysis highlights the efficacy of our bug mitigation strategy in elevating code quality across all models, even outside the SolMover framework.