More than Chit-Chat: Developing Robots for Small-Talk Interactions

• URL: http://arxiv.org/abs/2412.18023v1
• Date: Mon, 23 Dec 2024 22:35:38 GMT
• Title: More than Chit-Chat: Developing Robots for Small-Talk Interactions
• Authors: Rebecca Ramnauth, Dražen Brščić, Brian Scassellati,
• Abstract summary: Small talk plays a pivotal role in social dynamics, serving as a verbal handshake for building rapport and understanding. For conversational AI and social robots, the ability to engage in small talk enhances their perceived sociability. We introduce a novel method that autonomously generates feedback and ensures LLM-generated responses align with small talk conventions.
• Score: 1.9116784879310025
• License:
• Abstract: Beyond mere formality, small talk plays a pivotal role in social dynamics, serving as a verbal handshake for building rapport and understanding. For conversational AI and social robots, the ability to engage in small talk enhances their perceived sociability, leading to more comfortable and natural user interactions. In this study, we evaluate the capacity of current Large Language Models (LLMs) to drive the small talk of a social robot and identify key areas for improvement. We introduce a novel method that autonomously generates feedback and ensures LLM-generated responses align with small talk conventions. Through several evaluations -- involving chatbot interactions and human-robot interactions -- we demonstrate the system's effectiveness in guiding LLM-generated responses toward realistic, human-like, and natural small-talk exchanges.

Related papers

• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation [70.52558242336988]
  We focus on predicting engagement in dyadic interactions by scrutinizing verbal and non-verbal cues, aiming to detect signs of disinterest or confusion. In this work, we collect a dataset featuring 34 participants engaged in casual dyadic conversations, each providing self-reported engagement ratings at the end of each conversation. We introduce a novel fusion strategy using Large Language Models (LLMs) to integrate multiple behavior modalities into a multimodal transcript''
  arXiv  Detail & Related papers  (2024-09-13T18:28:12Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, integrating psychology-grounded principles of personality: social practice, consistency, and dynamic development. We incorporate personality traits directly into the model parameters, enhancing the model's resistance to induction, promoting consistency, and supporting the dynamic evolution of personality.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• LLM Roleplay: Simulating Human-Chatbot Interaction [52.03241266241294]
  We propose a goal-oriented, persona-based method to automatically generate diverse multi-turn dialogues simulating human-chatbot interaction. Our method can simulate human-chatbot dialogues with a high indistinguishability rate.
  arXiv  Detail & Related papers  (2024-07-04T14:49:46Z)
• A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation [38.227022474450834]
  We present an addressee estimation model with improved performance in comparison with the previous state-of-the-art. We also propose several ways to incorporate explainability and transparency in the aforementioned architecture.
  arXiv  Detail & Related papers  (2024-05-20T13:09:32Z)
• Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru [9.2526849536751]
  We introduce a fully-automated conversation system that leverages large language models (LLMs) to generate robot responses with expressive behaviors. We conduct a pilot study where volunteers chat with a social robot using our proposed system, and we analyze their feedback, conducting a rigorous error analysis of chat transcripts. Most negative feedback was due to automatic speech recognition (ASR) errors which had limited impact on conversations.
  arXiv  Detail & Related papers  (2024-02-18T12:35:52Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models [23.945922720555146]
  We propose a system to achieve incremental learning of complex behavior from natural interaction. We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6.
  arXiv  Detail & Related papers  (2023-09-08T13:29:05Z)
• Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks [0.0]
  Successful social interaction is closely coupled with the interplay between nonverbal perception and action mechanisms. Nonverbal gestures are expected to endow social robots with the capability of emphasizing their speech, or showing their intentions. Our research sheds a light on modeling human behaviors in social interactions, specifically, forecasting human nonverbal social signals during dyadic interactions.
  arXiv  Detail & Related papers  (2021-10-18T15:01:32Z)
• A MultiModal Social Robot Toward Personalized Emotion Interaction [1.2183405753834562]
  This study demonstrates a multimodal human-robot interaction (HRI) framework with reinforcement learning to enhance the robotic interaction policy. The goal is to apply this framework in social scenarios that can let the robots generate a more natural and engaging HRI framework.
  arXiv  Detail & Related papers  (2021-10-08T00:35:44Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

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.