人間の社会的決定を予測する大きな言語モデルの能力を評価する
Feng Xiao1, X T XiaoTian Wang2
1Department of Applied Psychology, School of Humanities and Social Science, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Boulevard, 518172, Shenzhen, China.
Scientific reports
|September 2, 2025
まとめ
大型言語モデル (LLM) は,人間とは異なる意思決定パターンを示し,特に社会的状況やリスクの好みを示しています. LLMは親族関係やグループサイズに 人間の感受性が欠けていて 心理的なチューリングテストの洞察力を提供しています
科学分野:
- 認知科学
- 人工知能
- 進化心理学
背景:
- 大型言語モデル (LLM) は人間の意思決定を予測する可能性を 示しています
- 人工知能の意思決定が 人間の心理と一致することを理解することは 人工知能の開発に不可欠です
研究 の 目的:
- LLM (GPT-3.5,GPT-4,GPT-4o) の予測と,様々なシナリオにおける人間の決定を比較する.
- LLMのリスク偏好と親族関係やグループサイズなどの社会的要因に対する感受性を調査する.
- 意思決定パターンに基づく 心理的なチューリングテストの基準を特定する.
主な方法:
- 2104人の参加者のデータに対して51のシナリオで9,600のGPT-3. 5/ GPT-4の反応を比較した分析.
- 8つの社会グループと親族関係におけるGPT-4oの1600件の回答の評価
- 進化心理学の枠組みと展望理論の適用
主要な成果:
- 人間は LLM よりも 親族関係や 集団の大きさに敏感です
- LLMは人間とは異なるリスク探しのパターンを示し,しばしばプロスペクト理論の価値関数を逆転させます.
- GPT-4は一貫してリスク回避であり,GPT-4oはヒトと比較してグループサイズ効果に関して反対の予測を行った.
結論:
- LLMと人間は,特に社会的文脈とリスクに関して,異なる意思決定ヒューリスティクスを示しています.
- フレーム効果と社会的な状況によるリスク偏好は,心理的なチューリングテストの重要な差異点です.
- 人工知能が人間の社会的認知と意思決定に 適合するかを精査するには さらに研究が必要です
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