Development of a Trust-Aware User Simulator for Statistical Proactive
Dialog Modeling in Human-AI Teams
- URL: http://arxiv.org/abs/2304.11913v2
- Date: Sun, 18 Jun 2023 18:56:01 GMT
- Title: Development of a Trust-Aware User Simulator for Statistical Proactive
Dialog Modeling in Human-AI Teams
- Authors: Matthias Kraus, Ron Riekenbrauck, Wolfgang Minker
- Abstract summary: The concept of a Human-AI team has gained increasing attention in recent years.
For effective collaboration between humans and AI teammates, proactivity is crucial for close coordination and effective communication.
We present the development of a corpus-based user simulator for training and testing proactive dialog policies.
- Score: 4.384546153204966
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The concept of a Human-AI team has gained increasing attention in recent
years. For effective collaboration between humans and AI teammates, proactivity
is crucial for close coordination and effective communication. However, the
design of adequate proactivity for AI-based systems to support humans is still
an open question and a challenging topic. In this paper, we present the
development of a corpus-based user simulator for training and testing proactive
dialog policies. The simulator incorporates informed knowledge about proactive
dialog and its effect on user trust and simulates user behavior and personal
information, including socio-demographic features and personality traits. Two
different simulation approaches were compared, and a task-step-based approach
yielded better overall results due to enhanced modeling of sequential
dependencies. This research presents a promising avenue for exploring and
evaluating appropriate proactive strategies in a dialog game setting for
improving Human-AI teams.
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) - 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) - Real-time Addressee Estimation: Deployment of a Deep-Learning Model on
the iCub Robot [52.277579221741746]
Addressee Estimation is a skill essential for social robots to interact smoothly with humans.
Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot.
The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
arXiv Detail & Related papers (2023-11-09T13:01:21Z) - Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review [6.013543974938446]
Leveraging Artificial Intelligence in decision support systems has disproportionately focused on technological advancements.
A human-centered perspective attempts to alleviate this concern by designing AI solutions for seamless integration with existing processes.
arXiv Detail & Related papers (2023-10-30T17:46:38Z) - Interactive Natural Language Processing [67.87925315773924]
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP.
This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
arXiv Detail & Related papers (2023-05-22T17:18:29Z) - Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task
Dialogues [0.716879432974126]
Graph neural networks (GNNs) show a remarkable superiority by reaching a success rate above 80% with only 50 dialogues, when learning from simulated experts.
We suggest to concentrate future research efforts on bridging the gap between human data, simulators and automatic evaluators in dialogue frameworks.
arXiv Detail & Related papers (2023-02-22T08:18:49Z) - Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with
User Simulator [37.590563896382456]
We propose an interactive evaluation framework for Task-Oriented Dialogue (TOD) systems.
We first build a goal-oriented user simulator based on pre-trained models and then use the user simulator to interact with the dialogue system to generate dialogues.
Experimental results show that RL-based TOD systems trained by our proposed user simulator can achieve nearly 98% inform and success rates.
arXiv Detail & Related papers (2022-10-26T07:41:32Z) - Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy
Evaluation Approach [84.02388020258141]
We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning.
ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation.
Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
arXiv Detail & Related papers (2021-02-20T03:29:20Z) - Adaptive Dialog Policy Learning with Hindsight and User Modeling [10.088347529930129]
We develop algorithm LHUA that, for the first time, enables dialog agents to adaptively learn with hindsight from both simulated and real users.
Experimental results suggest that, in success rate and policy quality, LHUA outperforms competitive baselines from the literature.
arXiv Detail & Related papers (2020-05-07T07:43:43Z) - 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.