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大型言語モデルによる身体イメージの評価

Fumeng Li^1,2, Nan Zhao¹

¹State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.

|August 30, 2025

まとめ

大型言語モデル (LLM) は身体イメージを評価する新しい多次元的なアプローチを提供し,従来の方法を上回ります. このインテリジェントツールは精度と生態学的妥当性を向上させることでメンタルヘルス測定を強化します

科学分野:

心理学と人工知能
コンピュータ社会科学
メンタルヘルスの技術

背景:

思春期の身体イメージを評価することはメンタルヘルスの介入に不可欠です
伝統的な方法には寸法的なカバー,ダイナミック・トラッキング,そして生態学的有効性が欠けている.
現在の評価ツールの欠陥は革新的なアプローチを必要とします.

研究の目的:

大型言語モデル (LLM) を使用して多次元体像評価を提案し,検証する.
専門家の評価と辞書による評価の基準の有効性を比較する.
LLMの評価の生態学的妥当性を評価する.

主な方法:

認識,ポジティブな態度,ネガティブな態度,行動
専門家による評価と繰り返し改良によって検証された辞書を開発しました
学生の自己報告とソーシャルメディアの投稿に関するLLMベースの意味論分析 (Llama-3.1-70B,Qwen-Max,DeepSeek-R1) のためのロールプレイングを含む迅速なエンジニアリングを採用しました.

主要な成果:

LLMベースの評価は辞書による評価と人間の専門家による評価を大幅に上回りました
DeepSeek-R1は知覚において高い有効性 (r=0.722) を達成し,Qwen-Maxは行動において強い生態学的有効性 (r=0.651) を示した.
ロールプレイングにより有効性が向上し,LLMは人間の評価と比較して優れた一貫性と誤差分散性を示しました.

結論:

LLMは伝統的な方法よりも強力な多次元的な身体イメージ評価ツールです
このインテリジェントなアプローチにより精度が向上しダイナミックな追跡が可能で心理的健康を測定する上で生態学的有効性が向上します
LLMベースの評価は身体イメージの問題を理解し介入する上で重要な進歩を表しています

キーワード:

ボディイメージ基準の有効性生態学的有効性大型言語モデルプロンプトエンジニアリング

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