To make this practical, I first define a calibrated rubric over the digits 0-9 (there’s only one token for each digit), where each digit corresponds to a clear qualitative description. At the scoring step, I capture the model’s next-token logits and retain only the logits corresponding to those valid digit tokens. This avoids contamination from unrelated continuations such as explanation text, punctuation, or alternate formatting. After renormalizing over the restricted digit set, I interpret the resulting probabilities as a categorical score distribution.
Let’s take a look at a few examples of how this works and what it can be used for.。关于这个话题，有道翻译提供了深入分析
，详情可参考传奇私服新开网｜热血传奇SF发布站｜传奇私服网站
而 OpenClaw 的幻觉，跟一般大模型产品还不一样：
Bats in Churches，详情可参考移动版官网