Papers by Jonathan Ivey
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |
NUTMEG: Separating Signal From Noise in Annotator Disagreement (2025.emnlp-main)
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| Challenge: | Recent work suggests that annotators may have genuine disagreements, but few models separate signal from noise in annotator disagreement. |
| Approach: | They propose a Bayesian model that removes noisy annotations from training data while preserving systematic disagreements. |
| Outcome: | The proposed model outperforms models trained on NUTMEG-aggregated data. |