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

Published 1 Nov 2024 in cs.CL, cs.AI, and cs.HC | (2411.00927v2)

Abstract: LLM-based agents are increasingly employed to interact with external environments (e.g., games, APIs, world models) to solve user-provided tasks. However, current frameworks often lack the ability to collaborate effectively with users in fully conversational settings. Conversations are essential for aligning on task details, achieving user-defined goals, and satisfying preferences. While existing agents address ambiguity through clarification questions, they underutilize the broader potential of an LLM's conversational capabilities. In this work, we introduce ReSpAct, an LLM-based agent designed to seamlessly integrate reasoning, decision-making, and dynamic dialogue for task-solving. Expanding on reasoning-first approaches like ReAct, ReSpAct employs active, free-flowing dialogues to interpret instructions, clarify goals, provide status updates, resolve subtask failures, and refine plans based on user inputs without any explicit dialogue schema. By alternating between task-solving actions and interactive conversations, ReSpAct demonstrates improved performance across diverse environments. We evaluate ReSpAct in user-interactive settings, including task-oriented dialogue systems (MultiWOZ) and decision-making tasks (ALFWorld, WebShop). ReSpAct outperforms ReAct with absolute success rate improvements of 6% and 4% in ALFWorld and WebShop, respectively, and achieves a 5.5% gain in Inform and a 3% gain in Success scores in MultiWOZ. These results highlight the value of integrating dynamic user-agent collaboration for more effective task resolution.

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Summary

  • The paper introduces the ReSpAct framework, which dynamically fuses reasoning, speaking, and acting to significantly improve conversational AI performance.
  • It leverages continuous user interaction to clarify ambiguities and update strategies, achieving success rate gains of 6% (AlfWorld) and 4% (WebShop).
  • The framework paves the way for more intuitive, human-like AI agents by challenging traditional models with robust, feedback-driven decision making.

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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