We introduce a dynamic benchmarking system for conversational agents that evaluates their performance through a single, simulated, and lengthy user$\leftrightarrow$agent interaction. The interaction is a conversation between the user and agent, where multiple tasks are introduced and then undertaken concurrently. We context switch regularly to interleave the tasks, which constructs a realistic testing scenario in which we assess the Long-Term Memory, Continual Learning, and Information Integration capabilities of the agents. Results from both proprietary and open-source Large-Language Models show that LLMs in general perform well on single-task interactions, but they struggle on the same tasks when they are interleaved. Notably, short-context LLMs supplemented with an LTM system perform as well as or better than those with larger contexts. Our benchmark suggests that there are other challenges for LLMs responding to more natural interactions that contemporary benchmarks have heretofore not been able to capture.

本研究提出了一种动态基准测试系统，用于评估对话智能体的性能，重点关注长期记忆、持续学习和信息整合能力。研究发现，尽管大型语言模型在单任务交互中表现良好，但在多个任务交替进行时却面临挑战，这揭示了当前基准测试未能捕捉到的自然互动中的更多挑战。

超越提示：大型语言模型的动态对话基准测试