The field of scientific research is experiencing significant developments with the integration of large language models (LLMs). Researchers are exploring the potential of LLMs to improve various aspects of the research process, including post-publication reviews, citation practices, and academic writing. Studies have shown that LLMs can generate high-quality written works, such as related work sections and summaries, but may also introduce biases and inaccuracies. For instance, LLMs have been found to overgeneralize scientific conclusions, posing a risk of misinterpretations of research findings. Furthermore, LLMs may reinforce existing citation patterns, such as the Matthew effect, and influence the trajectory of scientific discovery. Noteworthy papers in this area include: ScholarCopilot, which introduces a unified framework for generating professional academic articles with accurate citations, and Generalization Bias in Large Language Model Summarization of Scientific Research, which highlights the potential risks of LLMs in misinterpreting research findings. These developments have significant implications for the scientific community, and further research is needed to fully understand the potential benefits and limitations of LLMs in scientific research.