AIモデルは再帰的に生成されたデータで訓練されると崩壊します
Ilia Shumailov1, Zakhar Shumaylov2, Yiren Zhao3
1OATML, Department of Computer Science, University of Oxford, Oxford, UK. ilia.shumailov@chch.ox.ac.uk.
Nature
|July 24, 2024
まとめ
独創的な人工知能 (AI) のモデルでは,モデル崩壊という現象で,不可逆的な欠陥が発生します. これはAIによって生み出される 未来のコンテンツの質と多様性に 影響を及ぼします
科学分野:
- 人工知能
- 機械学習
- 生成モデル
背景:
- GPT-4のような大型言語モデル (LLM) と 安定した拡散のような画像生成モデルを含む 生成型人工知能 (AI) は オンラインコンテンツを急速に変化させています
- AIで生成されたテキストと画像の普及は,これらのモデルを訓練するために使用されるデータの将来性について疑問を投げかけています.
- GPT-2,GPT-3,GPT-4などの以前のAIの進歩は,さまざまな言語のタスクにおいて重要な能力を示しています.
研究 の 目的:
- 大型言語モデル (LLM) が将来のAIトレーニングデータに与える潜在的な影響を調査する.
- モデルによって生成されたコンテンツでAIモデルが訓練されたときに発生する欠陥を特定し,分析する.
- これらの欠陥がAI開発の持続可能性と多様なデータソースの価値に及ぼす影響を理解する.
主な方法:
- LLM,バリエーションオートエンコーダー (VAE),ガウス混合モデル (GMM) を含むジェネラティブモデルの理論分析.
- モデル崩壊の発生と影響を実証するシミュレーションと経験的研究.
- 合成データでモデルを訓練すると,データ分布の喪失に関する調査が終了します.
主要な成果:
- 訓練におけるモデル生成コンテンツの無差別な使用は,AIモデルの不可逆的な欠陥につながります.
- この現象は"モデル崩壊"と呼ばれ,元のデータ分布の尾の消失を引き起こします.
- モデル崩壊は,LLM,VAE,GMMを含む様々なタイプの生成モデルにおいて,普遍的な問題であることが示されています.
結論:
- モデル崩壊はAIによって生み出されるコンテンツの 長期的な品質と多様性に重大な脅威をもたらします
- 大規模なウェブデータに関するトレーニングの利点を維持するには,モデルの崩壊に対処する必要があります.
- 人工知能の訓練における モデルの崩壊に対する 対策として リアルな人間のやり取りを反映したデータが 益々価値を持つようになるでしょう
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