促进有效的混合人-LLM推理和决策.
Andrea Passerini1, Aryo Gema2, Pasquale Minervini2
1Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
Frontiers in artificial intelligence
|January 23, 2025
概括
这一观点凸显了对人类-大型语言模型 (LLM) 协作进行更多研究的需要. 专注于人-LLM互动可以改善AI.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 现代大型语言模型 (LLM) 展示了令人印象深刻的功能,但也表现出非微不足道的错误.
- 尽管对LLM的局限性进行了大量研究,但人类与LLM合作的动态和影响仍未得到充分探索.
研究的目的:
- 为优先考虑人类与LLM互动的研究辩论.
- 检查阻碍有效的人机协作的偏见.
- 讨论提高人类LLM推理和决策的目标.
主要方法:
- 视角的作品检查现有文献,并提出未来的研究方向.
- 分析影响人类与LLM合作的偏见.
- 讨论潜在的解决方案和未来的研究目标.
主要成果:
- 目前的研究不足以解决人与LLM合作的潜力和风险.
- 偏见可能会阻碍人类和LLMs之间的有效合作.
结论:
- 未来的LLM研究应该优先加强人与LLM的互动.
- 实现相互理解和互补的团队表现是有效的人类-LLM推理的关键目标.
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