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.
Linguistic Monoculture in LLM-Assisted Language Use
arXiv:2607. 27134v1 Announce Type: new Abstract: Writing and communication are increasingly mediated by large language models (LLMs) that are being used to draft, revise and polish text.
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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 24
When Preferences Fail to Become Incentives: A Utility-Behavior Gap in Large Language Models
arXiv:2606. 22974v2 Announce Type: replace Abstract: Recent work on preference elicitation in large language models (LLMs) has demonstrated that, when given a series of choices between two outcomes, LLMs reveal a coherent, model-specific utility structure.
arXiv AI
Jun 12
Authorship Attribution in Multilingual Machine-Generated Texts
arXiv:2508. 01656v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult.
arXiv Machine Learning
Jul 31
Epistemic diversity across language models mitigates knowledge collapse
arXiv:2512. 15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems.
arXiv AI
Jun 17
Do Large Language Models Always Tell The Same Stories?
arXiv:2606. 17350v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have enabled the generation of high-quality prose, yet the question of whether these models are capable of generating diverse outputs remains contested.
arXiv AI
Jul 24
Artificial Epanorthosis: Why large language models overuse a classical rhetorical figure, and how to mitigate it
arXiv:2607. 21498v1 Announce Type: cross Abstract: A rhetorical figure that Cicero and Quintilian catalogued two thousand years ago reappears, systematically, in the text of large language models: epanorthosis, the self-correction of the specimen {\guillemotleft}This is not a course.
Hugging Face Trending Papers
Jul 23
Artificial Epanorthosis: Why large language models overuse a classical rhetorical figure, and how to mitigate it
A rhetorical figure that Cicero and Quintilian catalogued two thousand years ago reappears, systematically, in the text of large language models: epanorthosis, the self-correction of the specimen «This is not a course. It is a journey of transformation».
arXiv Machine Learning
Jul 16
Graded Entity-Familiarity Readouts in Language Models: Polish Adaptation, Cross-Language Robustness, and Refusal Steering
arXiv:2607. 13568v1 Announce Type: cross Abstract: Can a language model estimate its familiarity with an entity before generating an answer?
arXiv AI
Jun 2
Isolating LLM Lexical Bias: A Curation-Free Triangulated Metric for Preference-Stage Learning
arXiv:2606. 00334v1 Announce Type: cross Abstract: Various language domains have undergone remarkable changes in recent years; these shifts are largely attributed to the advent of Large Language Models and their misalignment with natural language usage.
arXiv AI
Jul 17
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
arXiv:2510. 01171v4 Announce Type: replace-cross Abstract: Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse.
arXiv AI
Jul 24
Benchmarking the Personalization Capabilities of Large Language Models
arXiv:2607. 20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives.