arXiv:2609.20838v1 Announce Type: new
Abstract: In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-en...
By Zeynep \"Ozdemir, Murat Osmano\u{g}lu, Sevgi Yi\u{g}it-Sert, \"Omer \"Ozg\"ur Tanr{\i}\"over, Y{\i}lmaz Ar
The paper investigates how adjectival modifiers affect the semantic plausibility of events, using the Adept benchmark of 16,000 English sentence pairs that differ by a single adjective. Experiments show that sentence transformers, despite being conceptually suited to the task, underperform compared to models like RoBERTa. The authors provide an error analysis and discuss the implications of their findings for future work on balancing training and test data.
By Anna Golub, Beate Zywietz, Annerose Eichel
The paper presents an empirical study of factual errors in human-written text, focusing on corrections in newspaper articles to build a taxonomy of common mistakes such as kanji misconversions and unit errors. It evaluates large language models’ ability to detect these errors, finding that even advanced models like GPT‑5.4 achieve only a 52% word‑level F1 score on synthetic data, underscoring the difficulty of the task. The work highlights the gap in research on factual error detection in human writing compared to LLM hallucinations.
By Kazuma Iwamoto, Kazumasa Omura, Shotaro Ishihara
The paper introduces DETECT-REMASK-REPAIR, a diffusion-based method for updating outdated spans in existing summaries while keeping supported content intact. It identifies, masks, and repairs only the changed regions using masked diffusion language models. Experiments on DialogSum and a new StreamSum benchmark show that this localized repair improves faithfulness, reduces repair time to under half a second, and offers trade‑offs between faithfulness, speed, and preservation of the original summary.
By Hao Zou, Zachary Horvitz, Chandhru Karthick, Zhou Yu, Kathleen McKeown
arXiv:2607. 22657v1 Announce Type: cross Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing machine-unlearning algorithms can suppress this behavior.
By Viktoriia Makovska, George Fletcher
The paper introduces VOLM, a framework that quantifies how much original value a human adds to a document beyond what a language model could generate from a task description alone. Unlike existing tools that focus on stylistic detection, VOLM extracts content at varying granularities, reconstructs it with an LLM, and compares these reconstructions to those derived from the task description. Evaluations across news articles, ICLR peer reviews, and argumentative essays show that VOLM can distinguish human-authored texts from LLM-generated ones while remaining robust to content-preserving transformations.
By Vibhhu Sharma, Thorsten Joachims, Sarah Dean