Embracing Foundation Models for Advancing Scientific Discovery

Machine learning foundation models, particularly large language models (LLMs) such as GPT-4o, have revolutionized traditional applications in computer vision and natural language processing, marking a significant shift in recent years. Building on these advancements, recent efforts have explored the potential of foundation models in hypothesis generation, highlighting their possibility in aiding human researchers in scientific discovery. In this paper, we envision a future where academia increasingly integrates foundation models to accelerate and enhance the process of scientific discovery. Motivated by potential application scenarios of foundation models in scientific research, our vision is anchored in a central question: How can we accelerate scientific discovery with the aid of foundation models? To address this overarching question, we raise two key challenges that need to be addressed: (1) how to effectively harness the parametric knowledge embedded in foundation models to propel scientific discovery? and (2) how to develop rigorous yet scalable methods to evaluate the effectiveness of foundation models in supporting scientific research? To tackle these two challenges, we propose our approaches, termed knowledge-grounded Chain-of-Idea (KG-CoI) hypothesis generation and IdeaBench - Benchmarking LLM hypothesis generators in a customizable manner. Through addressing these challenges, we outline our vision in hope to inspire new ideas and innovations in harnessing foundation models for advancing scientific discovery, paving the way for a new era of research collaboration between humans and artificial intelligence.