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
Authors: KR McKee
Year: 2024
Published in: IEEE Transactions on Technology and Society, 2024 - ieeexplore.ieee.org
Institution: University of Queensland
Research Area: AI Ethics, Human-Computer Interaction, Research Practice Transparency
Discipline: AI Ethics, Human-Computer Interaction
The paper identifies ethical and transparency gaps in AI research involving human participants and proposes guidelines to address these issues, drawing from adjacent fields like psychology and human-computer interaction while recognizing unique challenges in AI contexts.
Methods: Analyzed normative practices by reviewing AI research publications and compared them with ethical standards in adjacent fields such as psychology and HCI.
Key Findings: Ethical practices including ethical reviews, informed consent, participant compensation, and contextual considerations specific to AI research.
DOI: https://ieeexplore.ieee.org/abstract/document/10664609/
Citations: 17
