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

• URL: http://arxiv.org/abs/2411.00927v1
• Date: Fri, 01 Nov 2024 15:57:45 GMT
• Title: ReSpAct: Harmonizing Reasoning, Speaking, and Acting Towards Building Large Language Model-Based Conversational AI Agents
• Authors: Vardhan Dongre, Xiaocheng Yang, Emre Can Acikgoz, Suvodip Dey, Gokhan Tur, Dilek Hakkani-Tür,
• Abstract summary: Large language model (LLM)-based agents have been increasingly used to interact with external environments. Current frameworks do not enable these agents to work with users and interact with them to align on the details of their tasks. This work introduces ReSpAct, a novel framework that combines the essential skills for building task-oriented "conversational" agents.
• Score: 11.118991548784459
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (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.

Related papers

• Simulating User Agents for Embodied Conversational-AI [9.402740034754455]
  We build a large language model (LLM)-based user agent that can simulate user behavior during interactions with an embodied agent. We evaluate our user agent's ability to generate human-like behaviors by comparing its simulated dialogues with the TEACh dataset.
  arXiv  Detail & Related papers  (2024-10-31T00:56:08Z)
• A Survey on Complex Tasks for Goal-Directed Interactive Agents [60.53915548970061]
  This survey compiles relevant tasks and environments for evaluating goal-directed interactive agents. An up-to-date compilation of relevant resources can be found on our project website.
  arXiv  Detail & Related papers  (2024-09-27T08:17:53Z)
• Hello Again! LLM-powered Personalized Agent for Long-term Dialogue [63.65128176360345]
  We introduce a model-agnostic framework, the Long-term Dialogue Agent (LD-Agent) It incorporates three independently tunable modules dedicated to event perception, persona extraction, and response generation. The effectiveness, generality, and cross-domain capabilities of LD-Agent are empirically demonstrated.
  arXiv  Detail & Related papers  (2024-06-09T21:58:32Z)
• Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents [110.25679611755962]
  Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. We introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users' implicit intentions through explicit queries. We empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires user intentions, and refines them into actionable goals.
  arXiv  Detail & Related papers  (2024-02-14T14:36:30Z)
• Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations [70.7884839812069]
  Large language models (LLMs) have emerged as powerful and general solutions to many natural language tasks. However, many of the most important applications of language generation are interactive, where an agent has to talk to a person to reach a desired outcome. In this work, we explore a new method for adapting LLMs with RL for such goal-directed dialogue.
  arXiv  Detail & Related papers  (2023-11-09T18:45:16Z)
• Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users [51.34484827552774]
  We release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. These dialogues reflect interesting dynamics of collaborative decision-making in task-oriented scenarios. We propose a novel task of multi-user contextual query rewriting: to rewrite a task-oriented chat between two users as a concise task-oriented query.
  arXiv  Detail & Related papers  (2023-10-31T14:12:07Z)
• AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems [112.76941157194544]
  We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
  arXiv  Detail & Related papers  (2023-10-13T16:37:14Z)
• Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models [52.24756457516834]
  We propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of Large Language Models (LLMs) This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts.
  arXiv  Detail & Related papers  (2023-09-22T15:41:34Z)
• Leveraging Explicit Procedural Instructions for Data-Efficient Action Prediction [5.448684866061922]
  Task-oriented dialogues often require agents to enact complex, multi-step procedures in order to meet user requests. Large language models have found success automating these dialogues in constrained environments, but their widespread deployment is limited by the substantial quantities of task-specific data required for training. This paper presents a data-efficient solution to constructing dialogue systems, leveraging explicit instructions derived from agent guidelines.
  arXiv  Detail & Related papers  (2023-06-06T18:42:08Z)
• User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems [65.88679683468143]
  We propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction. USDA incorporates the sequential transitions of both content and act features in the dialogue to predict the user satisfaction. Experimental results on four benchmark goal-oriented dialogue datasets show that the proposed method substantially and consistently outperforms existing methods on USE.
  arXiv  Detail & Related papers  (2022-02-07T02:50:07Z)
• Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems [47.45333978381135]
  We introduce the Action-Based Conversations dataset with over 10K human-to-human dialogues containing 55 distinct user intents. We propose two additional dialog tasks, Action State Tracking and Cascading Dialogue Success, and establish a series of baselines. Empirical results demonstrate that while more sophisticated networks outperform simpler models, a considerable gap (50.8% absolute accuracy) still exists to reach human-level performance on ABCD.
  arXiv  Detail & Related papers  (2021-04-01T22:04:25Z)
• LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization [2.78632567955797]
  Reinforcement learning can enable task-oriented dialogue systems to steer the conversation towards successful task completion. In an end-to-end setting, a response can be constructed in a word-level sequential decision making process with the entire system vocabulary as action space. Current approaches use an uninformed prior for training and optimize the latent distribution solely on the context. It is therefore unclear whether the latent representation truly encodes the characteristics of different actions.
  arXiv  Detail & Related papers  (2020-11-18T16:23:30Z)
• SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning [61.335252950832256]
  Sense-Plan-Ask, or SPA, generates plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication.
  arXiv  Detail & Related papers  (2020-02-08T23:15:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.