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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3
Self-Consistency（自己整合性）
3.5
Self-Consistency（自己整合性） [Wang et al. 2023] は、LLM に同じ問題を複数の方法で解
かせ、その回答を集約することで、回答の信頼性を高める手法です。この手法を用いることで
モデルの出力のばらつきを抑え、正確性を向上させることができます。
Wangらの論文 [Wang et al. 2023] では、Self-Consistency を適用する際の回答のマージ方法
として、多数決（Voting）と確率（Probability）の二つのアプローチが紹介されています。
1. 多数決（Voting）アプローチ：LLMが生成した複数の回答に対して、最も頻繁に出現する
回答を最終的な答えとして選択します。具体的には、次の手順で回答をマージします。
①LLMに同じ問題を複数回解かせ、回答を生成する。
②生成された回答をクラスタリングし、同じ意味の回答をグループ化する。
③各グループの回答数を集計し、最も多くの回答を含むグループを特定する。
④最も多くの回答を含むグループから、代表的な回答を最終的な答えとして選択する。
2. 確率（Probability）アプローチ：このアプローチでは、LLMが生成した複数の回答に対し
て、各回答の生成確率を計算し、確率が最も高い回答を最終的な答えとして選択します。
回答の生成確率は、回答に含まれる各トークンの生成確率を乗算することで計算すること
ができます ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.