Scideator: Human-LLM Scientific Idea Generation Grounded in Research-Paper Facet Recombination

A good idea should be relevant to the scientist's interests and novel within the scientific community. Research papers are a major source of inspiration for relevant and novel ideas, as they expose scientists to relevant concepts to re-combine and form new ideas [4, 21, 36]. However, generating relevant and novel scientific ideas by recombining concepts from research papers is difficult for multiple reasons. For one, scientists must wade through an ever-expanding scientific literature to find relevant concepts [2, 19]. Moreover, the phenomenon of fixation biases scientists against considering more diverse concepts and concept recombinations for their research; instead, they are predisposed to thinking about a problem in familiar terms, which hinders the stimulation of novel ideas [11, 37]. Even if a scientist manages to identify interesting concept recombinations to form potential research ideas, assessing the ideas' novelty in comparison to the existing literature is a cumbersome yet critical task. Building a fully or semi-automated ideation system has been an ambition of researchers for decades, and Scideatorbuilds on strong prior work from many other researchers, filling a unique niche. We extend a line of work that presents systems for finding analogies between research papers [4, 21, 36], adopting their facet-based framework but using modern large language model (LLM) methods to identify relevant facets and perform facet recombinations. We are also inspired by recent work showing that LLMs have promise to assist ideation in domains outside science, helping people to generate more ideas [6] and more diverse ideas [27, 40].