This paper investigates how Large Language Models (LLMs) handle dynamic user intent in conversations, where users' goals change over time, and finds that current LLMs struggle with this capability, leading to significant performance drops when evaluated in a more realistic setting.
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This paper develops a framework called NexForge that helps train more capable artificial agents by automatically generating a large number of tasks and training data, without requiring a lot of manual setup. Practitioners might care because it can improve the performance of their own agent models.