困惑性と近接性:大きな言語モデル困惑性は,不協和音の検出のための意味的距離メトリックを補完します
Weizhe Xu1, Serguei Pakhomov2, Patrick Heagerty3
1Biomedical Informatics and Medical Education (BIME), University of Washington, Seattle, WA, USA.
Journal of biomedical informatics
|August 23, 2025
まとめ
この研究では,統合失調症スペクトル障害 (SSD) の検出に役立つスピーチの一貫性を測定するために,大規模な言語モデル (LLM) を使用する新しい方法が紹介されています. LLMと伝統的なメトリックを組み合わせることで 混乱した思考の評価の精度が向上しました
科学分野:
- コンピュータ言語学
- 精神科
- 機械学習
背景:
- スピーチの意味合いは,統合失調症スペクトル障害 (SSD) の診断に不可欠な組織的な思考を反映しています.
- 混乱した思考は SSD の精神病の主要な症状です
- 意味合いを評価するための自動化されたツールは,早期発見と症状のモニタリングに役立ちます.
研究 の 目的:
- 大規模言語モデル (LLM) に基づく意味論的一貫性の新しい尺度を開発する.
- LLMベースのコヘランスメトリックと従来のベクトル距離メソッドを比較する.
- SSDにおける無秩序な思考の識別におけるLLMの難解性の有用性を評価する.
主な方法:
- LLMの複雑さを用いて"バッグ"と"チェーン"モデルを評価した.
- 人間のコヒーレンス評価を予測するために,困惑性と近接性の特徴を組み合わせた訓練された回帰モデル.
- 精神病の症状のある人のスピーチモデルとSSD患者の臨床インタビューを検証した.
主要な成果:
- LLMの難解性と近接性の特徴を使用したコンビネッドアプローチは,トレーニングセットのヒトの評価と最も高い相関性 (0.61) を達成しました.
- この組み合わせの方法は,テストセットの近接性特性を単独で上回った (0.54対0.52).
- LLMベースの難解度メトリックは,従来の近接度量に補完的な価値を示しました.
結論:
- セマンティックコヘレンスの評価のための新しいLLMベースの方法が開発され,検証されました.
- LLMの混乱と近接の特徴を組み合わせることで,スピーチの一貫性の自動評価が強化されます.
- これらの発見は,SSDの診断とモニタリングを改善するためのLLMの可能性を強調しています.
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