We explore the utility and limitations of large language models (LLMs) like ChatGPT and their integration with Bing in various scientific research tasks. The sources demonstrate how LLMs can assist with extracting information from papers, interpreting data from figures, evaluating research, and refining scientific writing, including drafting titles and translating texts. They also investigate the LLMs' capabilities in designing experiments and public surveys, while cautioning against "hallucination" (providing incorrect information), inconsistent responses, and data privacy concerns. Ultimately, the texts highlight LLMs as powerful tools for scientific productivity, emphasizing the need for user validation and adherence to publishing guidelines.