Jonathan Ivey
Papers
1
Total Citations
2
H-Index
1
About
Jonathan Ivey is a researcher at the forefront of computational social science and human-AI interaction, with a primary focus on the fidelity of large language models (LLMs) in simulating human dialogue. His most cited work, "Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue" (2024), tackles a critical methodological challenge: the growing reliance on LLMs as proxies for human participants in dialogue research. Ivey systematically evaluates whether these models can replicate nuanced human qualities—such as emotional depth, conversational coherence, and social cues—finding significant gaps that caution against uncritical use. This contribution has already garnered attention (2 citations in its first year), highlighting its timely impact on research practices. By exposing the limitations of LLM-based simulations, Ivey’s work provides essential guidelines for designing more robust human-AI studies, saving researchers from costly missteps. His research bridges natural language processing, psychology, and ethics, offering both a practical toolkit for dataset creation and a theoretical framework for understanding machine-generated social behavior. For students and scholars, Ivey’s insights are indispensable for navigating the evolving landscape of human-computer dialogue.
Research Focus
Key Achievements
Top Papers
- 1