大型言語モデルでは 人間の会話をシミュレートできるのか?
Eric Mayor1, Lucas M Bietti2, Adrian Bangerter3
1Department of Psychology, University of Basel.
Cognitive science
|September 1, 2025
まとめ
大型言語モデル (LLM) は,人間の電話会話と比較して,誇張されたアライメントと不適切な会話マーカーの使用を示しています. 現在のLLMは,口頭での対話を一貫して効果的にシミュレートしていません.
科学分野:
- コンピュータ言語学
- 人工知能
- 人とコンピュータの相互作用
背景:
- 大型言語モデル (LLM) は,特にチャットベースのインタラクションにおいて,人間の認知を模倣する高度な能力を示しています.
- 発話された人間の会話をシミュレートするLLMの能力は,パラダイムシフトとしての可能性にもかかわらず,ほとんど未知のままです.
研究 の 目的:
- 大型言語モデル (LLM) が人間の会話を正確にシミュレートできる程度を調査する.
- LLMで生成された会話と人間の電話会話の言語的特徴を比較する.
主な方法:
- 研究1: スイッチボード (SB) コルパスのトランスクリプト (人間の電話会話) と,特別のプロンプトを使用してLLM (GPT-4,Claude Sonnet 3.5,Vicuna,Wayfarer) によって生成されたトランスクリプトを比較した.
- 分析は,アライメント (概念的,構文的,語彙的),協調マーカー,会話の開閉に焦点を当てた.
- 研究2: LLMで生成されたトランスクリプトと人間のSBトランスクリプトを区別する人間の能力を評価した.
主要な成果:
- LLMの会話は 誇張されたアライメントを示し 人間の会話とは違って 会話が進むにつれ 増加しました
- LLMは,調整マーカーの異なる,しばしば不適切な使用と,異なる会話の開始と終了を示しました.
- 人間の評価者は,LLMによって生成されたトランスクリプトと人間の会話を区別することができ,LLMは一貫して人間の対話に合格しなかったことを示しています.
結論:
- 現在のLLMによって生み出される会話は 人間の会話とは 質的にも 量的にも異なります
- 差異は,口頭対話とチャット間の固有の区別,またはLLMの訓練と能力の制限から生じることがあります.
- 将来のLLMとトレーニングデータの進歩により,シミュレーションは改善されるかもしれないが,根本的な違いは残るかもしれない.
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