对社会情报数据集的全面分析以及大型语言模型 (LLM) 与人类之间的响应趋势
Erika Mori1,2, Yue Qiu1, Hirokatsu Kataoka1
1National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8560, Japan.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
目前的人工智能很难理解人类的互动,缺乏可解释性. 新的评估揭示了人工智能和人类反应之间的重大差距,指导了未来人工智能开发的自然沟通.
科学领域:
- 人工智能的人工智能
- 人与计算机的交互
- 社会机器人社会机器人社会机器人
背景情况:
- 社会智能是自然的人类-AI互动和沟通的关键.
- 现有的数据集,如社会智商有局限性,包括简单的问答格式和缺乏答案的理由.
- 当前的人工智能方法往往缺乏解释性,因为直接选择答案而没有中间输出.
研究的目的:
- 在基于视频的人际交互的质量保证基准上全面评估人工智能方法.
- 分析人工智能对人类社会线索和互动的理解.
- 确定当前AI基准和评估社会智能的方法中的缺陷.
主要方法:
- 在基于视频的问答 (QA) 基准上对人工智能方法的评估.
- 利用与人类反应相关的额外注释进行更深入的分析.
- 对人工智能和人类反应模式的比较分析.
主要成果:
- 在理解社会互动方面,观察到人类和人工智能反应模式之间的显著差异.
- 目前的基准测试在评估人工智能的社会智能方面存在严重缺陷.
- 人工智能方法在为人类互动场景生成可解释和可靠的响应方面存在局限性.
结论:
- 调查结果强调了对人工智能社会智能的更先进数据集和评估方法的需求.
- 这项研究是实现人类和人工智能之间更自然,更无的沟通的一步.
- 未来的研究应该专注于提高AI的可解释性和可靠性,以了解复杂的人类互动.
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