February 2025
Intermediate
376 pages
9h 25m
Japanese
Content preview from 仕組みからわかる大規模言語モデル 生成AI時代のソフトウェア開発入門
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りんご: 0.4
いちご: 0.3
バナナ: 0.2
みかん: 0.1
これにtemperature として1、0.5、2 を適用した場合の確率分布は図1.5.1 のようになりま
す。
図1.5.1
図1.5.1 の左端は、temperatureが 1の場合の確率分布を示しています。この場合、確率分
布はそのまま使用されます。中央はtemperatureが 0.5 の場合の確率分布です。確率分布が尖
り、確率の高いトークンが選択されやすくなることがわかります。右端は temperature が 2 の
場合の確率分布です。確率分布が平坦になり、確率の低いトークンも選択されやすくなること
がわかります。
このようにtemperature を調整することで、生成されるテキストの確定性（毎回同じような
結果が得られる性質）や多様性を調整することができます。低いtemperature では確定性のあ
る出力が得られ、高いtemperature では多様性のある出力が得られます。
計算方法
LLM API の内部では、与えられた文脈に基づいて次のトークンの確率分布 ...
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