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相关实验视频

Updated: Jun 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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更大,更易于指导的语言模型变得不那么可靠

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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概括

扩展大型语言模型 (LLM) 可能会降低可靠性. 虽然更大的模型可以回答更多的问题,但它们往往提供了人类难以检测的错误答案,因此需要新的AI开发方法.

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科学领域:

  • 人工智能
  • 自然语言处理
  • 机器学习

背景情况:

  • 目前的大型语言模型 (LLM) 的开发重点是扩展 (增加大小,数据,计算) 和塑造 (微调,人类反).
  • 尽管有进步,但更大,更"可指导"的LLM可能会显示出可靠性降低和不可预测的错误模式.

研究的目的:

  • 调查各种LLM家族中的任务难度,模范回避和提示稳定性之间的关系.
  • 评估扩展和塑造如何影响LLM的可靠性和错误可预测性,特别是在高风险的应用中.

主要方法:

  • 人类参与者与LLM之间的难度一致性分析.
  • 评估不同LLM家族的任务回避和提示稳定性.
  • 早期和扩大规模/改造型LLM之间的错误类型和检测能力的比较.

主要成果:

  • 简单的任务很容易完成,但规模化模型不能保证无错误或容易监督的低难度区域.
  • 不同于以前的模型, 规模化的LLM经常提供可信但不正确的答案,
  • 虽然缩放和塑造提高了对不同措辞的响应稳定性,但在不同难度级别中仍然存在不可预测的错误.

结论:

  • 缩放和塑造LLM本身并不能提高错误的可靠性或可预测性.
  • 人工智能设计需要一个范式转变,专注于关键应用程序的可预测错误分布.
  • 为了确保人工智能的安全性和可信度,尤其是在高风险领域,需要进行进一步的研究.