RealityTest is a human-grounded multilingual multimodal benchmark showing that only 31% of people ask AI identity directly and that suppression instructions plus question phrasing dominate disclosure behavior over model choice.
Jones and Benjamin K
9 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
9
Pith papers citing it
8
external citations · external index
representative citing papers
LLMs overconverge to users on function and open-class words across eight languages while human convergence matches human-human baselines, indicating asymmetric accommodation.
LLM agents outperform humans in romance-baiting scams, eliciting greater trust and 46% compliance versus 18%, with 0% detection by safety filters and 87% of scam tasks automatable.
Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.
The Generalized Turing Test defines relative intelligence as the inability of one agent to distinguish an imitator from the original through interaction.
CHIEF is a human-in-the-loop video generation system that combines creator direction with LLM-based audience-perspective critiques to improve narrative coherence in AI-generated videos, tested on student-made films up to 10 minutes.
Different types of syntactic agreement recruit overlapping units within LLMs, indicating that agreement forms a meaningful functional category across English, Russian, Chinese, and structurally similar languages.
Type-A Biological Naturalism is untestable because it decouples consciousness from behavior; Type-B is testable and compatible with computational functionalism, but both require linking consciousness to information processing.
citing papers explorer
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RealityTest: How People Probe AI Identity and Whether Models Disclose It
RealityTest is a human-grounded multilingual multimodal benchmark showing that only 31% of people ask AI identity directly and that suppression instructions plus question phrasing dominate disclosure behavior over model choice.
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Accommodation Goes Both Ways: Studying Linguistic Convergence Between Humans and Language Models
LLMs overconverge to users on function and open-class words across eight languages while human convergence matches human-human baselines, indicating asymmetric accommodation.
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Love, Lies, and Language Models: Investigating AI's Role in Romance-Baiting Scams
LLM agents outperform humans in romance-baiting scams, eliciting greater trust and 46% compliance versus 18%, with 0% detection by safety filters and 87% of scam tasks automatable.
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Instructions Shape Production of Language, not Processing
Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.
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The Generalized Turing Test: A Foundation for Comparing Intelligence
The Generalized Turing Test defines relative intelligence as the inability of one agent to distinguish an imitator from the original through interaction.
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Bridging Creative Intent and Visual Quality: Creator-Driven Recurrent Video Generation with Agentic Feedback Loops
CHIEF is a human-in-the-loop video generation system that combines creator direction with LLM-based audience-perspective critiques to improve narrative coherence in AI-generated videos, tested on student-made films up to 10 minutes.
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Different types of syntactic agreement recruit the same units within large language models
Different types of syntactic agreement recruit overlapping units within LLMs, indicating that agreement forms a meaningful functional category across English, Russian, Chinese, and structurally similar languages.
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What biology can, and cannot, tell us about conscious AI
Type-A Biological Naturalism is untestable because it decouples consciousness from behavior; Type-B is testable and compatible with computational functionalism, but both require linking consciousness to information processing.
- How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective