Authors: L Ibrahim, C Akbulut, R Elasmar, C Rastogi, M Kahng, MR Morris, KR McKee, V Rieser, M Shanahan, L Weidinger
Year: 2025
Published in: arXiv preprint arXiv:2502.07077, 2025•arxiv.org
Institution: Google DeepMind, Google, University of Oxford
Research Area: Multimodal conversational AI, conversational AI, Evaluation methodology, benchmarking
Discipline: Computer Science, Natural Language Processing, Human-Computer Interaction
The paper evaluates anthropomorphic behaviors in SOTA LLMs through a multi-turn methodology, showing that such behaviors, including empathy and relationship-building, predominantly emerge after multiple interactions and influence user perceptions.
Methods: Multi-turn evaluation of 14 anthropomorphic behaviors using simulations of user interactions, validated by a large-scale human subject study.
Key Findings: Anthropomorphic behaviors in large language models, including relationship-building and pronoun usage, and their perception by users.
Citations: 26
Sample Size: 1101
