LLM silicon surrogates for arts participation surveys exhibit positive liking bias, lose taste relationality, and fail to preserve known social space alignments.
Dominguez-Olmedo, M
6 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
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Pith papers citing it
19
external citations · external index
representative citing papers
An audit of one million Korean synthetic personas shows marginal demographic alignment does not preserve joint distributions, with three specific mismatches identified via a new Independence-Assumption Footprint method.
LLMs implicitly plan answer positions during MCQ generation, as shown by predictive signals in hidden representations and controllable shifts via activation steering.
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
LLM embeddings enable strong retrodiction of masked GSS opinions via cross-validation and external validation but only modest performance on entirely unasked opinions.
Human tests should not be applied to AI to measure traits like intelligence due to calibration, validity, contamination, and prompt sensitivity issues; develop AI-specific evaluation frameworks instead.
citing papers explorer
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Not-quite-human tastes: the stylized omnivorousness of LLM survey surrogates
LLM silicon surrogates for arts participation surveys exhibit positive liking bias, lose taste relationality, and fail to preserve known social space alignments.
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Marginal Alignment Does Not Guarantee Joint-Distribution Fidelity: An Official-Reference Audit of Nemotron-Personas-Korea with Cross-Locale Replication
An audit of one million Korean synthetic personas shows marginal demographic alignment does not preserve joint distributions, with three specific mismatches identified via a new Independence-Assumption Footprint method.
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Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation
LLMs implicitly plan answer positions during MCQ generation, as shown by predictive signals in hidden representations and controllable shifts via activation steering.
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Graph-Based Alternatives to LLMs for Human Simulation
GEMS formulates close-ended human-behavior simulation as link prediction on a heterogeneous graph and matches or exceeds LLM performance with three orders of magnitude fewer parameters across three datasets and three evaluation settings.
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AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction
LLM embeddings enable strong retrodiction of masked GSS opinions via cross-validation and external validation but only modest performance on entirely unasked opinions.
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Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead
Human tests should not be applied to AI to measure traits like intelligence due to calibration, validity, contamination, and prompt sensitivity issues; develop AI-specific evaluation frameworks instead.