Personalising Idea Recommendation by Scientific Assistants

Abstract

Large Language Models (LLMs) are increasingly transforming how scientists approach research, with emerging tools supporting ideation, experimentation, and publication in attempts to expedite the research process. This work focuses on the foundational first step: generating novel, high-quality research ideas. Building on an existing LLM idea generation method, we introduce a system for personalised research idea generation that generates ideas by analysing a researcher’s publication history, providing tailored research recommendations. We evaluate the generated ideas via expert surveys across predefined metrics, offering insights into the system’s effectiveness and LLMs’ role in ideation in general. Findings show that personalised generation produces novel, interesting, and potentially impactful ideas comparable to baseline methods. Clarity remains a common limitation, while feasibility improves in the personalised variant, highlighting the potential of personalised LLM ideation.