The ability to accurately identify authorship is crucial for verifying content authenticity and
mitigating misinformation. Large Language Models (LLMs) have demonstrated an …

Peer review is fundamental to the integrity and advancement of scientific publication.
Traditional methods of peer review analyses often rely on exploration and statistics of …

Recently, there has been a growing interest in knowledge editing for Large Language
Models (LLMs). Current approaches and evaluations merely explore the instance-level …

Recent research demonstrates that the nascent fine-tuning-as-a-service business model
exposes serious safety concerns--fine-tuning over a few harmful data uploaded by the users …

The extensive utilization of large language models (LLMs) underscores the crucial necessity
for precise and contemporary knowledge embedded within their intrinsic parameters …

Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual
information in generated content, despite their superior capacities across tasks. Meanwhile …

Fine-tuning large language models (LLMs) on additional datasets is often necessary to
optimize them for specific downstream tasks. However, existing safety alignment measures …

Recent studies show that Large Language Models (LLMs) with safety alignment can be jail-
broken by fine-tuning on a dataset mixed with harmful data. First time in the literature, we …

Large Language Models (LLMs) have shown proficiency in question-answering tasks but
often struggle to integrate real-time knowledge, leading to potentially outdated or inaccurate …

In recent years, large language models (LLMs) have been widely adopted in political
science tasks such as election prediction, sentiment analysis, policy impact assessment, and …