CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships

2026-08-31

AI Summary

研究通过创建CompanionSim模拟框架生成合成对话数据，以评估人类对AI伴侣行为的感知，发现某些群体中AI伴侣行为降低了其喜爱度、人性度和信任度。

Abstract

Many people now see AI systems as not just productivity tools but as social companions. Researchers are eager to study the consequences of AI companionship behaviors, such as validation, which evoke trust, empathy, and attachment in human-human interaction. However, human-AI interaction data is limited and unreliable, slowing research progress. We scale small amounts of real-world data by simulating multi-turn human-chatbot dialogue across a range of chatbot behaviors and use cases. We release CompanionSim: a simulation framework with 2,240 simulated human-chatbot conversations representing 16 chatbot behaviors across seven use cases. Human participants annotated the simulated conversations and real-world conversations in two experiments probing perceptions of companionship behaviors. We conducted Study 1 with a U.S. representative sample ($N_{1}~=~628$) and Study 2 across the U.S., U.K., India, and Nigeria ($N_{2}~=~3,646$). Surprisingly, we find that companionship behaviors reduced likability, humanlikeness, and trust in AI chatbots. These effects were larger in particular subgroups: women and older participants saw companionship chatbots as less likable, humanlike, and trustworthy. We encourage researchers to leverage real-world and synthetic data together to study the differential impacts of AI companions and to create benchmark evaluations of AI chatbots.

Problem

• Anthropomorphism
• Human-AI Relationships
• Companionship Behaviors
• Synthetic Data

Innovation

• CompanionSim
• Synthetic Data
• Anthropomorphism
• Human-AI Interaction