ReSpAct Harmonizing Reasoning, Speaking, and Acting
Large language model (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 a 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 sub-task 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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Vardhan Dongre
Suvodip Dey
Gokhan Tur
