MLLMs given the same instructions as human participants achieve expert-level performance on perceiving stress in network visualizations and rely on similar visual proxies.
In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
3 Pith papers cite this work. Polarity classification is still indexing.
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Demographic-only LLM agents for retirement survey prediction exhibit central tendency bias, fail to reproduce incorrect or 'don't know' answers, and miss factor interactions in regressions, unlike survey-anchored agents.
RECOVER is an LLM-powered RPM system for postoperative GI cancer care, built from 7 participatory design sessions and 5 patient interviews, then piloted with 4 staff and 5 patients to derive design strategies and responsible AI insights.
citing papers explorer
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Exploring MLLMs Perception of Network Visualization Principles
MLLMs given the same instructions as human participants achieve expert-level performance on perceiving stress in network visualizations and rely on similar visual proxies.
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From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes
Demographic-only LLM agents for retirement survey prediction exhibit central tendency bias, fail to reproduce incorrect or 'don't know' answers, and miss factor interactions in regressions, unlike survey-anchored agents.
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RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative Gastrointestinal Cancer Care
RECOVER is an LLM-powered RPM system for postoperative GI cancer care, built from 7 participatory design sessions and 5 patient interviews, then piloted with 4 staff and 5 patients to derive design strategies and responsible AI insights.