As a researcher, reading and understanding scientific papers has always been a crucial part of my daily routine. I still remember the tricks I learned in grad school for how to digest a paper efficiently. However, with countless research papers being published every day, I felt overwhelmed to keep up to date with the latest research trends and insights. The old tricks I learned can only help so much.
Things start to change with the recent development of large language models (LLMs). Thanks to their remarkable contextual understanding capability, LLMs can fairly accurately identify relevant information from the user-provided documents and generate high-quality answers to the user’s questions about the documents. A myriad of document Q&A tools have been developed based on this idea and some tools are designed specifically to assist researchers in understanding complex papers within a relatively short amount of time.
Although it’s definitely a step forward, I noticed some friction points when using those tools. One of the main issues I had is prompt engineering. Since the quality of LLM responses depends heavily on the quality of my questions, I often found myself spending quite some time crafting the “perfect” question. This is especially challenging when reading papers in unfamiliar research fields: oftentimes I simply don’t know what questions to ask.
This experience got me thinking: is it possible to develop a system that can automate the process of Q&A about research papers? A system that can distill key points from a paper more efficiently and autonomously?
Previously, I worked on a project where I developed a dual-chatbot system for language learning. The concept there was simple yet effective: by letting two chatbots chat in a user-specified foreign language, the user could learn the practical usage of the language by simply observing the conversation. The success of this project led me to an interesting thought: could a similar dual-chatbot system be useful for understanding research papers as well?