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ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents (2411.00927v1)

Published 1 Nov 2024 in cs.CL, cs.AI, and cs.HC

Abstract: LLM-based agents have been increasingly used to interact with external environments (e.g., games, APIs, etc.) and solve tasks. However, current frameworks do not enable these agents to work with users and interact with them to align on the details of their tasks and reach user-defined goals; instead, in ambiguous situations, these agents may make decisions based on assumptions. This work introduces ReSpAct (Reason, Speak, and Act), a novel framework that synergistically combines the essential skills for building task-oriented "conversational" agents. ReSpAct addresses this need for agents, expanding on the ReAct approach. The ReSpAct framework enables agents to interpret user instructions, reason about complex tasks, execute appropriate actions, and engage in dynamic dialogue to seek guidance, clarify ambiguities, understand user preferences, resolve problems, and use the intermediate feedback and responses of users to update their plans. We evaluated ReSpAct in environments supporting user interaction, such as task-oriented dialogue (MultiWOZ) and interactive decision-making (AlfWorld, WebShop). ReSpAct is flexible enough to incorporate dynamic user feedback and addresses prevalent issues like error propagation and agents getting stuck in reasoning loops. This results in more interpretable, human-like task-solving trajectories than relying solely on reasoning traces. In two interactive decision-making benchmarks, AlfWorld and WebShop, ReSpAct outperform the strong reasoning-only method ReAct by an absolute success rate of 6% and 4%, respectively. In the task-oriented dialogue benchmark MultiWOZ, ReSpAct improved Inform and Success scores by 5.5% and 3%, respectively.

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Overview of "ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building LLM-Based Conversational AI Agents"

This paper presents the ReSpAct framework, an innovative approach to developing conversational AI agents that effectively integrate reasoning, speaking, and acting capabilities. The authors focus on the limitations of current LLM-based agents, which often rely on assumptions in situations of ambiguity, resulting in errors and inefficiencies. The ReSpAct framework expands upon the ReAct methodology, emphasizing continuous interaction with users to align on task details, incorporate feedback, and update plans dynamically.

The core contribution of ReSpAct is enabling agents to engage in meaningful dialogues, thereby enhancing their problem-solving abilities by incorporating user insights and preferences. This capability allows agents to clarify ambiguities, seek guidance, and refine their strategies, ultimately leading to improved task-solving trajectories that are more interpretable and human-like. The framework was evaluated using GPT-4 within environments such as task-oriented dialogue (MultiWOZ) and interactive decision-making settings (AlfWorld, WebShop).

Strong Numerical Results

ReSpAct demonstrates notable improvements in task completion metrics compared to baseline reasoning-only approaches like ReAct. In AlfWorld and WebShop benchmarks, ReSpAct yields absolute success rate improvements of 6\% and 4\%, respectively. For the task-oriented dialogue benchmark MultiWOZ, ReSpAct improves the Inform and Success scores by 5.5\% and 3\%, respectively. These results underscore the efficacy of integrating reasoning with dynamic user interaction for task-oriented conversational agents.

Theoretical and Practical Implications

From a theoretical standpoint, ReSpAct provides a structured approach to bridge the gap between reasoning and user interaction in AI systems. The framework illustrates the importance of dialogue in context-aware decision-making processes and challenges traditional models that operate in isolation from user feedback. Practically, ReSpAct demonstrates significant potential for applications requiring nuanced human-machine interaction, such as virtual assistants, customer service bots, and autonomous navigation systems.

Future Developments in AI

ReSpAct sets the stage for further exploration into conversational AI agents that can seamlessly transition between reasoning, speaking, and acting. Future developments might focus on enhancing stateful policies to improve dialogue precision, ensuring better task alignment and completion. Additionally, integrating more advanced user simulation techniques could further refine these agents' training, making them adaptable to increasingly complex real-world scenarios.

Conclusion

By addressing the limitations of current frameworks and fostering enhanced human-agent collaboration, the ReSpAct framework marks a significant step forward in the development of LLM-based conversational AI agents. It effectively demonstrates how synergizing reasoning, speaking, and acting can lead to more efficient, user-aligned task completion, paving the way for more sophisticated and intuitive AI systems.

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Authors (6)
  1. Vardhan Dongre (8 papers)
  2. Xiaocheng Yang (11 papers)
  3. Emre Can Acikgoz (11 papers)
  4. Suvodip Dey (10 papers)
  5. Gokhan Tur (47 papers)
  6. Dilek Hakkani-Tür (164 papers)