The paper_search tool, developed by Baiyi Jian, is designed to assist users in searching academic papers on arXiv. Utilizing a combination of Python, DALL-E, browser, and plugins_prototype tools, this GPT provides an efficient solution for users looking to navigate the wealth of information available on arXiv. With a focus on enhancing the search experience and enabling users to discover relevant academic content, paper_search ensures researchers and academics can easily access and explore the latest findings in their field.
How to use
To utilize paper_search effectively, follow these steps:
- Access the tool through the designated platform.
- Enter the keywords or parameters relevant to your search.
- Review the search results and navigate through the suggested academic papers.
- Explore further details or refine your search criteria as needed.
Features
- Efficient academic paper search on arXiv
- Integration of Python, DALL-E, browser, and plugins_prototype tools
- Enhanced search experience for researchers and academics
- Facilitates access to the latest academic content in various fields
Updates
2024/01/31
Language
Chinese (中文 (Zhōngwén), 汉语, 漢語)
Tools
- python
- dalle
- browser
- plugins_prototype
Tags
public
reportable
uses_function_calls