让对话代理通过记忆学习用户偏好，提升长期协作效果
MultiSessionCollab: Learning User Preferences with Memory to Improve Long-Term Collaboration
- 设计带记忆的长期协作代理，跨会话学习用户偏好
- 实验显示记忆机制使任务成功率更高、交互更高效
- 真实用户测试验证记忆能显著改善用户体验
随着对话代理与用户多次协作积累经验，适应用户偏好对建立长期关系和提升协作质量至关重要。我们提出 MultiSessionCollab 基准，用于评估代理在多轮会话中学习用户偏好并持续改进协作的能力。为此，我们开发了具备专用记忆模块的长期协作代理，可跨会话学习用户偏好并优化交互。此外，我们证明可通过用户模拟器行为提取学习信号，以训练代理生成更全面的反思并更有效地更新记忆。大量实验表明，配备该记忆机制的代理能随时间提升协作表现，实现更高的任务成功率、更高效的交互过程，并降低用户投入。最后，人类用户研究进一步证实，在真实场景中，记忆机制显著提升了用户满意度。
原文摘要 · Abstract (English)
As conversational agents accumulate experience collaborating with users, adapting to user preferences is essential for fostering long-term relationships and improving collaboration quality over time. We introduce MultiSessionCollab, a benchmark that evaluates how well agents can learn user preferences and leverage them to improve collaboration quality throughout multiple sessions. To develop agents that succeed in this setting, we present long-term collaborative agents equipped with a memory that is specifically designed to learn user preferences across sessions and improve interactions. Moreover, we demonstrate that learning signals can be derived from user simulator behavior in MultiSessionCollab to train agents to generate more comprehensive reflections and update their memory more effectively. Extensive experiments show that equipping agents with our memory improves collaboration over time, yielding higher task success rates, more efficient interactions, and reduced user effort. Finally, we conduct a human user study that demonstrates that memory helps improve user experience in real-world settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。