Do Large Language Models with Reasoning and Acting Meet the Needs of Task-Oriented Dialogue?

• URL: http://arxiv.org/abs/2412.01262v1
• Date: Mon, 02 Dec 2024 08:30:22 GMT
• Title: Do Large Language Models with Reasoning and Acting Meet the Needs of Task-Oriented Dialogue?
• Authors: Michelle Elizabeth, Morgan Veyret, Miguel Couceiro, Ondrej Dusek, Lina M. Rojas-Barahona,
• Abstract summary: We apply the ReAct strategy to guide large language models (LLMs) performing task-oriented dialogue (TOD) While ReAct-LLMs seem to underperform state-of-the-art approaches in simulation, human evaluation indicates higher user satisfaction rate compared to handcrafted systems.
• Score: 10.464799846640625
• License:
• Abstract: Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations. However, they underperform compared to previous approaches in task-oriented dialogue (TOD), wherein reasoning and accessing external information are crucial. Empowering LLMs with advanced prompting strategies such as reasoning and acting (ReAct) has shown promise in solving complex tasks traditionally requiring reinforcement learning. In this work, we apply the ReAct strategy to guide LLMs performing TOD. We evaluate ReAct-based LLMs (ReAct-LLMs) both in simulation and with real users. While ReAct-LLMs seem to underperform state-of-the-art approaches in simulation, human evaluation indicates higher user satisfaction rate compared to handcrafted systems despite having a lower success rate.

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