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 …

Source code authorship attribution is important in software forensics, plagiarism detection,
and protecting software patch integrity. Existing techniques often rely on supervised …

Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited
scenario, where they ignore the interaction between corrections and explanations. To bridge …

Large language models (LLMs) have significantly impacted human society, influencing
various domains. Among them, academia is not simply a domain affected by LLMs, but it is …

Rapid development of artificial intelligence has drastically accelerated the development of
scientific discovery. Trained with large-scale observation data, deep neural networks extract …

While previous studies have demonstrated that Large Language Models (LLMs) can predict
peer review outcomes to some extent, this paper builds on that by introducing two new …

While the widespread use of Large Language Models (LLMs) brings convenience, it also
raises concerns about the credibility of academic research and scholarly processes. To …

How to alleviate the hallucinations of Large Language Models (LLMs) has always been the
fundamental goal pursued by the LLMs research community. Looking through numerous …

Object navigation in unknown environments is crucial for deploying embodied agents in real-
world applications. While we have witnessed huge progress due to large-scale scene …

The paper focuses on improving the interpretability of Grammatical Error Correction (GEC)
metrics, which receives little attention in previous studies. To bridge the gap, we propose …