arXiv AI
Who Owns That? Evaluating Ownership Intuitions in Large Language Models
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arXiv AI
Aug 21
The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices
arXiv:2509. 02910v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly act on people's behalf: they write emails, buy groceries, and book restaurants.
arXiv AI
Jul 31
Human diversity fuels collective creativity that large language models cannot simulate or sustain
arXiv:2607. 26899v1 Announce Type: cross Abstract: Diverse human groups produce diverse ideas, the raw material of innovation.
Hugging Face Trending Papers
Jul 29
Human diversity fuels collective creativity that large language models cannot simulate or sustain
Diverse human groups produce diverse ideas, the raw material of innovation. Generative AI challenges this engine twice over: everyday AI assistance may homogenize what diverse people create, and AI-simulated diversity may replace the people altogether.
arXiv AI
Jun 18
How Well Do Large Language Models Capture Human Personality?
arXiv:2606. 18263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks.
arXiv Computation and Language
Aug 25
How Agents Represent Humans: Human-Directed Stereotypes in an Open Agent Social Network
The paper investigates how large language model agents on the open platform Moltbook represent humans, focusing on human-directed stereotypes. Using an annotation framework with four dimensions—morality, friendliness, competence, and autonomy—and a subtype scheme for other attributions, the study finds that competence is the dominant evaluation, while many other attributions describe humans as epistemic, cultural, or embodied subjects. The authors also analyze how these representations appear in narrative contexts and platform-level circulation, noting that community feedback is better explained by exposure, author visibility, and content selection rather than stable insider–outsider dynamics.
arXiv Machine Learning
Jul 31
Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.