The paper investigates how language models transform factual news into stories, revealing that standard source‑agreement metrics misjudge intentional changes. By rewriting 2,000 news articles into summaries and stories, the authors measured alterations across the five Ws, finding that stories often lose place and causation details while increasing accountability distortions—sometimes downplaying perpetrators. The study introduces ReTold‑2k, a corpus and scoring protocol to detect and mitigate such distortions.
The paper investigates how language models transform factual news into narrative stories, revealing that standard source‑agreement metrics misjudge the quality of these retellings. By having three model families rewrite 2,000 news articles as summaries and as stories, the authors measured changes across the five Ws and identified distortions—especially in place, causation, and accountability—that are often overlooked by fact‑checking. They found that stories tend to lose critical contextual details while amplifying accountability distortions, which can shift blame and remain undetected by conventional checks.
By Kwangsuk Park, JiWoong Yang, Vaniushkina Kseniia
The paper introduces FOVEATED, a plug‑and‑play framework that improves atomic‑fact recall in unstructured knowledge editing (UKE) for large language models. By randomly shifting Rotary Position Embedding (RoPE) positions during editing, FOVEATED creates focused views of each sentence, counteracting the context‑reliance problem where edited LLMs reproduce passages but fail to recall individual facts. Experiments show consistent gains across five editors, two LLM backbones, and three benchmarks.
By Ding Wu, Ye Zhang, Haoyu Wang, Tianci Liu
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 activation steering—adding a vector to a large language model’s residual-stream activations—alters discourse-level aspects of generated text, such as formality, evidence usage, and ambiguity. It demonstrates that these unintended changes can be largely mitigated by applying a second, complementary steering vector that targets the affected discourse dimension, restoring about 78% of the original discourse characteristics while maintaining the intended behavior in roughly 93% of outputs.
By Shivam Adarsh, Hetianyu Huang, Lau Safin, Maria Maistro, Christina Lioma
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