了解计算对话理解理解计算对话
1German Research Center for Artificial Intelligence (DFKI), Alt-Moabit 91c, D-10559 Berlin, Germany.
概括
人工智能 (AI) 在与人类类似的对话理解斗争. 虽然大型语言模型显示出希望,但它们有局限性,需要进一步研究认知AI和系统架构.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算语言学 计算语言学
背景情况:
- 50多年来,对话系统已经从封闭域发展到开放域,多模式和多语言应用.
- 人工智能的近期进展,特别是大型语言模型 (LLM),使对话理解引起了公众和政治关注.
- 了解类似人类的对话仍然是人工智能的重大挑战,促使与人类大脑语言处理进行比较.
研究的目的:
- 解释人工智能在实现类似人类对话理解方面所面临的困难.
- 审查对话系统的历史发展和当前状态.
- 批判性地评估LLM,像ChatGPT,作为通往先进对话能力的途径.
主要方法:
- 五十年来对话系统发展的回顾.
- 对测试人工智能对话理解方法的分析.
- 查特GPT的案例研究,以确定当前LLM方法的局限性.
主要成果:
- 对于当前的人工智能来说,类似人类的对话理解是复杂和具有挑战性的.
- 法律法规代表了一个潜在的里程碑,但尚未相当于人类的理解,表现出局限性.
- 未来对话系统的关键系统架构原则包括对称的多模式,表示和预测反循环.
结论:
- 在AI中实现真正的人类式对话理解方面,仍然存在重大挑战.
- 未来的进步可能涉及交互式机器学习和解决诸如对话最大限度和多语言等重大挑战.
- 人工智能对话系统的发展对于诸如"欧洲语言平等法案"等倡议至关重要.
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