From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues

• URL: http://arxiv.org/abs/2410.03870v1
• Date: Fri, 4 Oct 2024 19:06:24 GMT
• Title: From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues
• Authors: Yu Li, Devamanyu Hazarika, Di Jin, Julia Hirschberg, Yang Liu,
• Abstract summary: Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. We show significant differences in these two types of responses and propose transitioning from one type to the other. We introduce Pix2Persona, a novel dataset aimed at developing ethical and engaging AI systems in various embodiments.
• Score: 29.549159419088294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. Our study systematically analyzes self-anthropomorphic expression within various dialogue datasets, outlining the contrasts between self-anthropomorphic and non-self-anthropomorphic responses in dialogue systems. We show significant differences in these two types of responses and propose transitioning from one type to the other. We also introduce Pix2Persona, a novel dataset aimed at developing ethical and engaging AI systems in various embodiments. This dataset preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropomorphic for each original bot response. Our work not only uncovers a new category of bot responses that were previously under-explored but also lays the groundwork for future studies about dynamically adjusting self-anthropomorphism levels in AI systems to align with ethical standards and user expectations.

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