Automated academic research papers

TEXT_INPUT:Ask the user to input the topic or research question.;;HTTP_REQUEST:Fetch relevant scholarly articles from academic databases based on the input topic or research question.;;CHATGPT:Prompt ChatGPT to summarize the fetched articles into a coherent introduction for the research paper.;;DALL_E:Generate a relevant image or visual representation related to the research topic.;;DISPLAY_OUTPUT:Display the summarization from ChatGPT, along with the fetched articles, and the generated image as an output for the user.

Large Language Models (LLMs) can automate academic paper creation in several ways. For instance, researchers can feed relevant keywords to LLMs, like ChatGPT, which can then generate detailed and accurate write-ups. Users can then refine and edit the output to suit their work. Also, LLMs can be used for literature review, synthesizing insights from large dataset of research papers to create well-curated literature summaries.

Generative AI could also be used to create figures and diagrams for research publication. Tools like DALL-E from OpenAI, which creates images from textual descriptions, can turn abstract research ideas into clear, communicative visualizations. These automated tools free up time for researchers to focus on analysis and interpretation, thus speed up the pace of academic research advancement.

How to build with Clevis

The application 'Automated academic research papers' is an excellent example of what you can design using Clevis, a tool for building AI applications. The aim of this application is to simplify the creation of academic research papers by automating key parts of the process.

The application gets started by requesting the user to input the subject or research question they are interested in (Text Input). Following this, it initiates an HTTP Request (Http Request) to access scholarly articles from academic databases that are relevant to the user's specified topic or research question.

The application then uses ChatGPT, an AI developed by OpenAI, to convert the fetched articles into a succinct and coherent introduction for the research paper (ChatGPT). The next step involves the utilization of DALL-E, another OpenAI innovation, to produce a pertinent image or visual portrayal related to the research question (DALL·E).

Finally, the application collates all the processed information and photographs and presents them to the user as a complete output (Display Output). This output consists of the summarized introduction from ChatGPT, the scholarly articles, and the generated image.

Through the process explained, you can appreciate how Clevis allows you to build applications that can transform complex procedures into streamlined, user-friendly experiences.