Summary of Respact: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-based Conversational Ai Agents, by Vardhan Dongre et al.
ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents
by Vardhan Dongre, Xiaocheng Yang, Emre Can Acikgoz, Suvodip Dey, Gokhan Tur, Dilek Hakkani-Tür
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Large language model (LLM)-based agents have been increasingly used to interact with external environments and solve tasks. However, current frameworks do not enable these agents to work with users and align on the details of their tasks and reach user-defined goals; instead, they may make decisions based on assumptions. This paper introduces ReSpAct, a novel framework that synergistically combines essential skills for building task-oriented “conversational” agents. ReSpAct 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 update their plans based on intermediate feedback. We evaluated ReSpAct in environments supporting user interaction, such as task-oriented dialogue (MultiWOZ) and interactive decision-making (AlfWorld, WebShop). ReSpAct outperforms the strong reasoning-only method ReAct by an absolute success rate of 6% and 4%, respectively, in two interactive decision-making benchmarks. In the task-oriented dialogue benchmark MultiWOZ, ReSpAct improved Inform and Success scores by 5.5% and 3%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about making computers smarter at understanding and working with humans. Right now, these “conversational” agents can solve problems on their own, but they don’t know how to work with people to get things done together. The researchers created a new system called ReSpAct that lets agents talk to users, figure out what needs to be done, and do it together. This system is good at asking for help when needed, understanding what the user wants, and making plans based on feedback. The researchers tested ReSpAct in three different scenarios and found that it works better than a simpler approach called ReAct. |
Keywords
» Artificial intelligence » Large language model
