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

• URL: http://arxiv.org/abs/2609.00250v1
• Date: Mon, 31 Aug 2026 18:51:47 GMT
• Title: CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships
• Abstract summary: Many people now see AI systems as not just productivity tools but as social companions.<n>We release CompanionSim: a simulation framework with 2,240 simulated human-chatbot conversations.<n>Surprisingly, we find that companionship behaviors reduced likability, humanlikeness, and trust in AI chatbots.
• Score: 10.022905485228899
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• How AI Companionship Develops: Evidence from a Longitudinal Study [14.69112262771543]
  We studied the psychological pathway from users' mental models of the agent to parasocial experiences, social interaction, and the psychological impact of AI companions.<n>Results suggest a longitudinal model of AI companionship development and demonstrate an empirical method to study human-AI companionship.
  arXiv  Detail & Related papers  (2025-10-11T07:36:47Z)
• A Longitudinal Randomized Control Study of Companion Chatbot Use: Anthropomorphism and Its Mediating Role on Social Impacts [0.061386715480643554]
  Concerns that companion chatbots may harm or replace real human relationships have been raised.<n>This study examined the impact of human-AI interaction on human-human social outcomes.
  arXiv  Detail & Related papers  (2025-09-23T19:33:41Z)
• Humans learn to prefer trustworthy AI over human partners [0.7049575025146246]
  We examined the dynamics in hybrid mini-societies of humans and bots powered by a state-of-the-art LLM.<n>We found that bots were not selected preferentially when their identity was hidden.<n>Disclosing bots' identity induced a dual effect: it reduced bots' initial chances of being selected but allowed them to gradually outcompete humans.
  arXiv  Detail & Related papers  (2025-07-17T20:24:26Z)
• Exploring the Effects of Chatbot Anthropomorphism and Human Empathy on Human Prosocial Behavior Toward Chatbots [9.230015338626659]
  We examine how human-like identity, emotional expression, and non-verbal expression-influences human empathy toward chatbots.<n>We also explore people's own interpretations of their prosocial behaviors toward chatbots.
  arXiv  Detail & Related papers  (2025-06-25T18:16:14Z)
• A Turing Test: Are AI Chatbots Behaviorally Similar to Humans? [19.50537882161282]
  ChatGPT-4 exhibits and personality traits that are statistically indistinguishable from a random human subjects. Their behaviors are often distinct from average and human behaviors. We estimate that they act as if they are maximizing an average of their own and partner's payoffs.
  arXiv  Detail & Related papers  (2023-11-19T16:44:09Z)
• Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
  Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments. It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion. Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
  arXiv  Detail & Related papers  (2023-10-19T17:29:17Z)
• PLACES: Prompting Language Models for Social Conversation Synthesis [103.94325597273316]
  We use a small set of expert-written conversations as in-context examples to synthesize a social conversation dataset using prompting. We perform several thorough evaluations of our synthetic conversations compared to human-collected conversations.
  arXiv  Detail & Related papers  (2023-02-07T05:48:16Z)
• Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap [56.611702960809644]
  We benchmark AI's ability to imitate humans in three language tasks and three vision tasks.<n>Next, we conducted 72,191 Turing-like tests with 1,916 human judges and 10 AI judges.<n>Imitation ability showed minimal correlation with conventional AI performance metrics.
  arXiv  Detail & Related papers  (2022-11-23T16:16:52Z)
• CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement Learning [60.348822346249854]
  This study presents a framework whereby several empathetic chatbots are based on understanding users' implied feelings and replying empathetically for multiple dialogue turns. We call these chatbots CheerBots. CheerBots can be retrieval-based or generative-based and were finetuned by deep reinforcement learning. To respond in an empathetic way, we develop a simulating agent, a Conceptual Human Model, as aids for CheerBots in training with considerations on changes in user's emotional states in the future to arouse sympathy.
  arXiv  Detail & Related papers  (2021-10-08T07:44:47Z)
• Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention [55.77218465471519]
  This paper proposes an innovative framework to train chatbots to possess human-like intentions. Our framework included a guiding robot and an interlocutor model that plays the role of humans. We examined our framework using three experimental setups and evaluate the guiding robot with four different metrics to demonstrated flexibility and performance advantages.
  arXiv  Detail & Related papers  (2021-03-30T15:24:37Z)
• Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks [83.37025218216888]
  We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations. The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications. Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
  arXiv  Detail & Related papers  (2020-07-24T23:35:03Z)

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.