创建,检索个人并为长期开放领域对话生成响应
Dohyun Cha1, Dawon Lee2, Jihie Kim1
1Department of Computer Science and Artificial Intelligence, Dongguk University, Seoul, Republic of South Korea.
PeerJ. Computer science
|September 24, 2025
概括
GRGPerDialogue框架通过从对话历史中生成演讲者人物来增强长期对话. 这种方法提高了响应的一致性和相关性,优于现有的对话系统.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 开放领域的对话系统在简短的对话中表现出色,但由于输入长度的限制,在长期的上下文中扎.
- 现有系统通常会在多会话聊天 (MSC) 中丢失早期对话中的关键信息,从而影响响应的一致性.
- 保持上下文对于在扩展互动中产生自然和一致的对话至关重要.
研究的目的:
- 提出一个新的框架,GRGPerDialogue,用于在长期对话中产生上下文相关和一致的响应.
- 在多会话聊天 (MSC) 中解决信息丢失的挑战,通过将演讲者个性化.
- 提高对话系统在扩展对话场景中的性能.
主要方法:
- 开发了一个三阶段的GRGPerDialogue框架:人格生成 (使用Llama 2 ICL),人格检索 (使用PUP数据集上的训练有素的Facebook密集通道检索模型) 和响应生成 (使用GPT-2和BART).
- 引入了一个新的数据集,Persona-Utterance Pair (PUP),以促进与人相关的发言检索.
- 利用上下文学习 (ICL) 来实现实时人物生成和密集段落检索 (DPR) 来实现高效的人物句子检索.
主要成果:
- GRGPerDialogue框架表现出优于基线模型的性能,在长期对话数据集上, Rouge-1分数大约有0.6%至1%的改善.
- 人类评估证实了拟议框架在产生流,一致和上下文相关的响应方面的有效性.
- 该框架成功地缓解了长期对话中上下文丢失的问题.
结论:
- GRGPerDialogue通过生成和检索个人身份有效地保存和利用历史对话信息.
- 拟议的框架显著提高了长期对话AI中响应的质量.
- GRGPerDialogue对于处理扩展交互的开放域对话系统来说是一个有希望的进步.
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