As a researcher, reading and understanding scientific papers has always been an important part of my daily life. I still remember the tricks I learned in grad school on how to digest papers effectively. However, with countless research papers being published every day, I feel overwhelmed to keep up with the latest research trends and insights. The old tricks I learned can only do so much.
This is starting to change with recent developments in large language models (LLMs). With its superior contextual understanding, LL.M. can identify relevant information from user-provided documents with considerable accuracy and generate high-quality answers to users' questions about the documents. Based on this idea, countless document question answering tools have been developed, some of which are specifically designed to help researchers understand complex papers in a relatively short period of time.
While this is definitely a step forward, I've noticed some friction points when using these tools. One of the main issues I'm having is swift engineering. Since the quality of the LL.M. responses depends heavily on the quality of my questions, I often find myself spending a considerable amount of time designing the "perfect" questions. This is especially challenging when reading papers in unfamiliar research fields: I often don't know what questions to ask.
This experience got me thinking: Is it possible to develop a system that can automate the question answering process for research papers? A system that can extract key points from papers more efficiently and autonomously?
Previously, I worked on a project to develop a dual chatbot system for language learning. The concept is simple but effective: by having two chatbots chat in a user-specified foreign language, users can learn how that language is actually used just by observing the conversation. The success of this project led me to an interesting thought: Could a similar dual chatbot system also help in understanding research papers?
So, in this blog post, we're going to bring that idea to life. Specifically, we will walk through the process of developing a dual chatbot system that autonomously digests research papers.
To make this journey a fun experience, we'll approach it as a software project and run one sprint: we'll start with "ideaing," introducing the concept of utilizing a dual chatbot system to solve our problem. Then "sprint execution" &