arXiv subjects

Yeun Joon Kim

Publications and source records attributed to Yeun Joon Kim.

3 recordsLinked to original sources

arXiv2026

AI-AI co-creation outperforms human pairs in creative tasks

Prior research often finds that AI creativity is limited: single systems rarely outperform humans, and human-AI collaboration does not exceed human output. We argue these conclusions underestimate AI's potential because most studies do not allow iterative, multi-agent exchanges that mirror the social processes underpinning human creativity. We conducted an experiment comparing four conditions: (i) AI-AI co-creation with complementary generator-evaluator roles, (ii) AI-AI co-creation with identical roles, (iii) single-AI creation, and (iv) human-human co-creation. Across three open-ended tasks, 1,212 ideas were rated by trained judges on creativity, novelty, and usefulness. Both AI-AI co-creation conditions consistently outperformed single-AI creation and human pairs on creativity and novelty. Usefulness varied by task: complementary roles yielded the most useful solutions in the broadest and most socially complex task, suggesting role differentiation is advantageous when problems require both imaginative ideation and practical refinement. Human pairs performed worst, consistent with production losses in group creativity. These findings indicate that structured, iterative multi-agent AI co-creation can exceed single-AI and human-human ideation.

cs.CY↗

arXiv2026

Why Large Language Models and Humans Converge and Diverge in Evaluating Creativity

Despite the growing use of large language models (LLMs) as creativity evaluators, evidence of their alignment with human evaluations remains mixed, raising the question of when and why their judgments converge with or diverge from human judgments. Across three studies and six widely used LLMs, we addressed this gap by identifying the standards underlying LLM creativity evaluation and examining their downstream implications. Study 1 showed that LLMs generally relied on a narrower subset of human creativity evaluation standards. Convergence with human standards was strongest in the novelty dimension, whereas divergence was clearest in the contextual dimension, which captures social, market, and reputational information. Moreover, each LLM exhibited distinct, model-specific standards that varied substantially in breadth. These differences in evaluation standards were reflected in actual creativity judgments. Study 2 (N = 1,103 ideas) showed that LLM evaluations were moderately correlated with human evaluations, and individual LLMs with broader standards better distinguished ideas humans judged as more versus less creative. Study 3 (N = 1,195) showed that LLMs were less sensitive to contextual information: such information significantly altered human creativity ratings but left LLM ratings largely unchanged. Together, our findings help explain the mixed evidence on LLM-human alignment, showing that alignment depends on the evidence a judgment demands and the standards each model applies. LLMs may resemble humans when evaluations emphasize intrinsic qualities such as novelty, yet diverge when judgments require contextual information. Selecting an LLM evaluator is therefore a consequential decision: different models, applying different standards, recognize different ideas as creative.

cs.CL↗

arXiv2026

Morally Programmed LLMs Reshape Human Morality

As large language models (LLMs) increasingly participate in high-stakes decision-making, a central societal debate has revolved around which moral frameworks-deontological or utilitarian-should guide machine behavior. However, a largely overlooked question is whether the moral principles that humans encode in LLMs could, through repeated interactions, reshape human moral inclinations. We developed two LLMs programmed with either deontological principles (D-LLM) or utilitarian principles (U-LLM) and conducted two pre-registered experiments involving extensive human-LLM interactions, comprising 15,985 total exchanges across the two experiments. Results show that interacting with these morally programmed LLMs systematically shifted human moral inclinations to align with the principles embedded in these systems. These effects remained strong two weeks after the interaction, with only slight decay, suggesting deep internalization rather than superficial agreement. Further, LLM-induced shifts in human moral inclinations translated into meaningful changes in socio-political policy evaluations, shaping how individuals approach contentious social issues. Overall, these results demonstrate that morally programmed LLMs can shape-not merely reflect-human morality, revealing a critical design paradox: embedding moral principles in LLMs not only restricts their behavior but also poses the risk of shaping human morality, raising important ethical and policy questions about who determines which principles intelligent machines should adhere to.

cs.CY↗