模型评测的局限 · 学 AI

解决什么问题What problem it solves

知道榜单分数不等于你的实际效果，这点很重要。A leaderboard score is not the same as your actual result — this matters.

这是什么What it is

公开评测难以覆盖你的具体任务，且存在数据污染、过时等问题。

Public evaluations struggle to cover your specific task, and suffer from data contamination and staleness.

为什么需要知道Why it matters

正确的做法是拿自己的真实任务去小样本实测，而不是只看榜单。The right approach is a small-scale test with your own real tasks rather than reading leaderboards alone.

接着读什么，为什么What to read next, and why

基准测试Benchmark
看数字之前补上适用范围与缺口。Check scope and omissions before interpreting scores.
联网 / 检索增强Retrieval-Augmented Generation, RAG
一起用于《怎么评测 RAG 的效果》Used together in “How to Evaluate a RAG System”
评测饱和Benchmark Saturation
一起用于《怎么自建评测口径》Used together in “How to Build Your Own Eval”

现在能做什么What you can do now

出处Sources

模型评测的局限Evaluation Limits