English(EN) My LLM Judge Caught 1 of 41 Wrong Answers. One Line Fixed the Rest.

LLM裁判在更改提示后捕获100%的错误

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-12 05:22

一位开发者测试了两个本地Ollama模型，llama3.2:1b和llama3.1:8b，以评估LLM在批改数学问题方面的能力。最初，当被简单要求验证提供的解决方案时，裁判模型未能识别出41个故意错误的答案中的任何一个。然而，在修改提示，指示裁判模型在将其解决方案与候选答案进行比较之前自行解决问题后，裁判模型成功识别出所有41个错误的答案。 AI

影响提示工程可以显著提高LLM裁判的准确性，使其在评估AI生成内容方面更加可靠。

排序理由该项目描述了一项关于LLM评估技术的实验及其发现。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

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Newsworthiness bucket: Tool; 该项目描述了一项关于LLM评估技术的实验及其发现。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

dev.to — LLM tag TIER_1 English(EN) · Priyansh Singhal · 2026-11-12 05:22

我的大语言模型裁判抓住了41个错误答案中的1个。一行代码修复了其余的。

<p>Asking the judge to solve the problem itself before grading turned a rubber stamp into a real check.</p> <p>I built a small local judge to grade a weaker model's math homework. I fed it 41 answers I had deliberately made wrong, expecting it to catch most of them. It caught one…

本文如何被排名

报道来源 [1]

我的大语言模型裁判抓住了41个错误答案中的1个。一行代码修复了其余的。

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