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    <title>Text Generation on Nalar</title>
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    <description>Recent content in Text Generation on Nalar</description>
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    <lastBuildDate>Sat, 05 Sep 2026 00:00:00 +0700</lastBuildDate>
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      <title>Understand Contrastive Decoding with Expert and Amateur Models</title>
      <link>https://nalar.dev/understand-contrastive-decoding-with-expert-and-amateur-models/</link>
      <pubDate>Sat, 05 Sep 2026 00:00:00 +0700</pubDate>
      <guid>https://nalar.dev/understand-contrastive-decoding-with-expert-and-amateur-models/</guid>
      <description>&lt;p&gt;A language model can assign high probability to text that is fluent but unhelpfully generic, repetitive, or too close to an easy pattern. Changing temperature or top-p changes how tokens are sampled from one model&amp;rsquo;s distribution, but it does not ask a different question: which candidate tokens are especially characteristic of a stronger model rather than a weaker one?&lt;/p&gt;&#xA;&lt;p&gt;&lt;strong&gt;Contrastive decoding&lt;/strong&gt; asks exactly that. It uses two language models at inference time: a stronger &lt;strong&gt;expert&lt;/strong&gt; and a weaker &lt;strong&gt;amateur&lt;/strong&gt;. A candidate is favored when the expert scores it well relative to the amateur, while a plausibility constraint prevents the decoder from choosing bizarre tokens merely because the amateur dislikes them even more.&lt;/p&gt;</description>
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