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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arXiv Computation and Language
Sep 2
Evaluating Style-Personalized Text Generation: Challenges and Directions
The paper "Evaluating Style-Personalized Text Generation: Challenges and Directions" examines the difficulties of assessing text that is tailored to individual users’ styles. It critiques common metrics such as BLEU, embeddings, and LLM-as-judges, and introduces a style discrimination benchmark covering domain discrimination, authorship attribution, and LLM-generated personalized versus non-personalized discrimination across eight writing tasks. The study finds that ensembles of diverse evaluation metrics outperform single-evaluator approaches and offers guidance for reliable assessment of style-personalized generation.
arXiv Computation and Language
Aug 24
When the Feature Pool Goes Algorithmic: Extending Mufwene's Ecology of Language Evolution to LLM-Mediated Exposure
arXiv:2608.21088v1 Announce Type: new Abstract: Mufwene's ecological model locates language evolution in competition among variants contributed by individual idiolects and in speakers' selection from...
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
Aug 24
Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)
arXiv:2605.27268v2 Announce Type: replace-cross Abstract: Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabu...
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 Computation and Language
6d ago
FAVoR: Measuring and Mitigating Author-Style Homogenization in Federated Personalized Generation
The paper introduces FAVoR, a method for federated personalized generation that mitigates author‑style homogenization caused by standard aggregation in parameter‑efficient fine‑tuning. Using the BlogText benchmark and ASCE diagnostics, the authors show that common federated PEFT baselines preserve semantic utility but blur author‑specific style. FAVoR employs a shared‑private adapter design, where clients upload shared updates while keeping author‑specific residual corrections locally, leading to improved style retention with minimal utility loss.
arXiv AI
Aug 21
When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Models
arXiv:2608. 19230v1 Announce Type: cross Abstract: As language models move from drafting prose to running literature-search agents with tool calls, fabricated references are becoming easier to catch and constrain.
arXiv Computation and Language
Sep 22
Privacy Personalization Trade offs in LLMs: The Impact of Stylometric Signal Reduction on User-Specific Text Generation
arXiv:2609.22112v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated the ability to generate user-specific text with high stylistic fidelity. However, the personal data that...
arXiv AI
4d ago
Population Fidelity: Evaluating Population Representativeness in LLMs
The paper introduces Population Fidelity, an evaluation framework for assessing how well large language models (LLMs) represent human population attitudes. It focuses on three dimensions: group-level accuracy, between-group variation, and the structure of that variation. Using the framework, the authors replicate a prior study on machine bias and test cultural fine-tuning, finding that while fine-tuning improves overall alignment, it does not enhance representation of within-population differences.
arXiv AI
3d ago
Who Owns That? Evaluating Ownership Intuitions in Large Language Models
arXiv:2609.39483v1 Announce Type: new Abstract: Ownership establishes rights over the use, control, and transfer of objects. Understanding these relations is essential for AI systems to interact appr...