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より大きく,より学習可能な言語モデルは信頼性が低下する.

Lexin Zhou1,2, Wout Schellaert1,3, Fernando Martínez-Plumed1,4

  • 1Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Valencia, Spain.

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まとめ
この要約は機械生成です。

大規模な言語モデル (LLM) のスケーリングは信頼性を低下させる可能性があります. より大きなモデルはより多くの質問に答えますが,しばしば誤った答えを出し,それを検出するのは人間にとって難しいので,新しいAI開発方法が必要になります.

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科学分野:

  • 人工知能
  • 自然言語処理
  • 機械学習

背景:

  • 現在の大型言語モデル (LLM) の開発は,スケーリング (サイズ,データ,計算の増大) とシェーピング (微調整,人間のフィードバック) に焦点を当てています.
  • 進歩にもかかわらず,より大きく,より"指示可能な"LLMは,信頼性が低下し,予測できないエラーパターンを示す可能性があります.

研究 の 目的:

  • 様々なLLMファミリーにおけるタスクの難しさ,モデル回避,および安定性を促す関係の調査.
  • LLMの信頼性とエラーの予測性,特に高リスクのアプリケーションにおけるスケーリングとシェーピングの影響を評価する.

主な方法:

  • 人間の参加者とLLMとの間の難易度の一致性の分析.
  • 異なるLLMファミリーのタスク回避とプロンプトの安定性を評価する.
  • 初期のLLMとスケールアップされたLLMのエラータイプと検出率の比較

主要な成果:

  • LLMは簡単なタスクを簡単に見つけますが,スケールモデルではエラーがなく,容易に監視できる低難易度のゾーンを保証できません.
  • スケール化されたLLMは,以前のモデルとは異なり,人間の監督者が見逃した難しい質問に対して,しばしば妥当だが誤った答えを提供します.
  • スケーリングとシェーピングは,さまざまなフレーズに対する応答の安定性を向上させますが,難易度を超えて予測できないエラーが持続します.

結論:

  • LLMのスケーリングとシェーピングは,本質的にエラーの信頼性や予測性を向上させません.
  • AI設計のパラダイムシフトが必要で,重要なアプリケーションの予測可能なエラー分布に焦点を当てている.
  • AIの安全性と信頼性を確保するために,特にリスクの高い分野では,さらなる研究が必要です.