Three New Research Papers Address LLM Applications in Wireless Networks, Data Annotation, and Security

Three New Research Papers Address LLM Applications in Wireless Networks, Data Annotation, and Security

Three recent preprints on arXiv explore diverse applications of large language models across different technical domains.

According to arXiv preprint 2512.22178v1, researchers are investigating the use of large language models for wireless traffic prediction in next-generation networks. The abstract notes that “the growing demand for intelligent, adaptive resource management in next-generation wireless networks has underscored the importance of accurate and scalable wireless traffic prediction,” building on recent advancements in deep learning and foundation models.

A second paper (arXiv:2512.22742v1) addresses Column Type Annotation (CTA), which the authors describe as “a fundamental step towards enabling schema alignment and semantic understanding of tabular data.” The research proposes using prompt augmentation with LoRA tuning to improve LLM-based column type annotation, noting that while existing encoder-only language models achieve high accuracy when fine-tuned, their applicability has limitations.

Finally, arXiv preprint 2412.10321v2 examines jailbreak attacks on LLMs. According to the abstract, many current attacks “rely on a common objective: making the model respond with the prefix ‘Sure, here is (harmful request)’.” The researchers identify two limitations with this straightforward approach, introducing “AdvPrefix” as an objective for more nuanced jailbreak techniques.