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English(EN) What smart-contract audits taught me about evaluating LLMs

LLM评估与智能合约审计相似,侧重于不变性和恶意输入

一位AI工程师将评估LLM与进行智能合约审计进行了类比,强调了识别最坏情况而非仅仅是理想情况的重要性。这种方法涉及为LLM系统编写“不变式”——即必须始终为真的属性,这与智能合约的测试方式类似。该工程师提倡将所有输入视为潜在的恶意输入,这与审计师假设用户和外部数据源存在恶意意图以减轻提示注入等风险的心态相呼应。 AI

影响 这种观点提出了一种更鲁棒的LLM评估方法,侧重于故障模式和安全性,以提高系统的可靠性和安全性。

排序理由 该条目是一篇工程师的观点文章,他从一个先前的领域借鉴类比到当前领域,提供了对LLM评估方法的视角。

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM评估与智能合约审计相似,侧重于不变性和恶意输入

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该条目是一篇工程师的观点文章,他从一个先前的领域借鉴类比到当前领域,提供了对LLM评估方法的视角。
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报道来源 [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Piyush Mali ·

    智能合约审计教会了我如何评估LLM

    <p>Before I worked on LLM systems, I spent a lot of my time in smart-contract audit contests. I entered more than twenty of them, on platforms like Sherlock, Code4rena and Immunefi. The setup is always similar: a team publishes its contracts, auditors get a limited window to read…