Cover Image

Benchmark of stylistic variation in LLM-generated texts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Milička, Jiří, Marklová, Anna, Cvrček, Václav
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AI Brown and AI Koditex: LLM-Generated Corpora Comparable to Traditional Corpora of English and Czech Texts
by: Milička, Jiří, et al.
Published: (2025)

Humans can learn to detect AI-generated texts, or at least learn when they can't
by: Milička, Jiří, et al.
Published: (2025)

Iconicity in Large Language Models
by: Marklová, Anna, et al.
Published: (2025)

Sydney Telling Fables on AI and Humans: A Corpus Tracing Memetic Transfer of Persona between LLMs
by: Milička, Jiří, et al.
Published: (2026)

The author is dead, but what if they never lived? A reception experiment on Czech AI- and human-authored poetry
by: Marklová, Anna, et al.
Published: (2025)

Theoretical and Methodological Framework for Studying Texts Produced by Large Language Models
by: Milička, Jiří
Published: (2024)

Simple stochastic processes behind Menzerath's Law
by: Milička, Jiří
Published: (2024)

Exposing propaganda: an analysis of stylistic cues comparing human annotations and machine classification
by: Faye, Géraud, et al.
Published: (2024)

Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation
by: Macko, Dominik, et al.
Published: (2025)

Stylometry recognizes human and LLM-generated texts in short samples
by: Przystalski, Karol, et al.
Published: (2025)

Are generative AI text annotations systematically biased?
by: Stolwijk, Sjoerd B., et al.
Published: (2025)

Can professional translators identify machine-generated text?
by: Farrell, Michael
Published: (2026)

Can postgraduate translation students identify machine-generated text?
by: Farrell, Michael
Published: (2025)

Differentially-private text generation degrades output language quality
by: Çano, Erion, et al.
Published: (2025)

ReadCtrl: Personalizing text generation with readability-controlled instruction learning
by: Tran, Hieu, et al.
Published: (2024)

MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning
by: Tran, Hieu, et al.
Published: (2025)

From text to multimodal: a survey of adversarial example generation in question answering systems
by: Yigit, Gulsum, et al.
Published: (2023)

Not all tokens are created equal: Perplexity Attention Weighted Networks for AI generated text detection
by: Miralles-González, Pablo, et al.
Published: (2025)

Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time
by: Herel, David, et al.
Published: (2024)

Benchmark Profiling: Mechanistic Diagnosis of LLM Benchmarks
by: Kim, Dongjun, et al.
Published: (2025)

<think> So let's replace this phrase with insult... </think> Lessons learned from generation of toxic texts with LLMs
by: Pletenev, Sergey, et al.
Published: (2025)

People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text
by: Russell, Jenna, et al.
Published: (2025)

Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations
by: Zhou, Yinghan, et al.
Published: (2025)

Gender Bias in LLM-generated Interview Responses
by: Kong, Haein, et al.
Published: (2024)

Learning to Summarize from LLM-generated Feedback
by: Song, Hwanjun, et al.
Published: (2024)

ReplicatorBench: Benchmarking LLM Agents for Replicability in Social and Behavioral Sciences
by: Nguyen, Bang, et al.
Published: (2026)

HalluLens: LLM Hallucination Benchmark
by: Bang, Yejin, et al.
Published: (2025)

Who Benchmarks the Benchmarks? A Case Study of LLM Evaluation in Icelandic
by: Ingimundarson, Finnur Ágúst, et al.
Published: (2026)

The power of text similarity in identifying AI-LLM paraphrased documents: The case of BBC news articles and ChatGPT
by: Xylogiannopoulos, Konstantinos, et al.
Published: (2025)

Semantic uncertainty in advanced decoding methods for LLM generation
by: Foodeei, Darius, et al.
Published: (2025)

Dataset Featurization: Uncovering Natural Language Features through Unsupervised Data Reconstruction
by: Bravansky, Michal, et al.
Published: (2025)

Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards
by: Tamber, Manveer Singh, et al.
Published: (2025)

Benchmarking and Improving LLM Robustness for Personalized Generation
by: Okite, Chimaobi, et al.
Published: (2025)

Spanish and LLM Benchmarks: is MMLU Lost in Translation?
by: Plaza, Irene, et al.
Published: (2024)

CreditAudit: 2$^\text{nd}$ Dimension for LLM Evaluation and Selection
by: Song, Yiliang, et al.
Published: (2026)

Abusive text transformation using LLMs
by: Chandra, Rohitash, et al.
Published: (2025)

LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient
by: Yuan, Peiwen, et al.
Published: (2025)

NC-Bench: An LLM Benchmark for Evaluating Conversational Competence
by: Moore, Robert J., et al.
Published: (2026)

Benchmark Test-Time Scaling of General LLM Agents
by: Li, Xiaochuan, et al.
Published: (2026)

Benchmarking of LLM Detection: Comparing Two Competing Approaches
by: Pröhl, Thorsten, et al.
Published: (2024)